{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目分析内容\n",
    "* 亚太地区各国家估值情况如何？\n",
    "* 亚太地区各国家独角兽的企业数量与国家估值之间的关系如何？\n",
    "* 分析美国各大城市的估值情况\n",
    "* 分析美国各行业的估值情况\n",
    "* 分析美国各城市随年份的变迁，城市估值、企业数量的变化？\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模块准备\n",
    "from flask import Flask, render_template, request\n",
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('hurun_unicorn.tsv', encoding='utf8', delimiter=\"\\t\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据-亚太地区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "序号                                                                      \n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1      2   字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2      3   滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "6      6    陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "10    11   微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "..   ...    ...            ...       ...  ..  ..    ...     ...   ...   \n",
       "486  264    有利网          Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "487  264   网易有道         Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "488  264   云鸟科技        Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "490  264  掌门1对1       Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "491  264     转转     Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                     部分投资机构  \n",
       "序号                           \n",
       "0            春华资本、中投海外、红杉资本  \n",
       "1       红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2    腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "6       摩根士丹利、中银集团、国泰君安（香港）  \n",
       "10              腾讯、华平投资、淡马锡  \n",
       "..                      ...  \n",
       "486          高瓴资本、晨兴资本、软银中国  \n",
       "487               君联资本、慕华投资  \n",
       "488          华平投资、红杉资本、经纬中国  \n",
       "490          顺为资本、达晨创投、华平投资  \n",
       "491                      腾讯  \n",
       "\n",
       "[222 rows x 10 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "亚太国家=[\"中国\",\"日本\",\"韩国\",\"朝鲜\",\"菲律宾\",\"马来西亚\",\"印度尼西亚\",\"新加坡\",\"澳大利亚\",\"新西兰\"]\n",
    "df.index.name=\"序号\"\n",
    "亚太=df[df['国家'].isin(亚太国家)]\n",
    "亚太"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>8230</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>4220</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>3910</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>2090</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>2060</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>1810</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>1460</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>1340</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1250</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>1190</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>870</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>740</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>大数据</th>\n",
       "      <td>720</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">中国</th>\n",
       "      <th>消费品</th>\n",
       "      <td>620</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>460</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>440</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>网络安全</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <th>云计算</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>物流</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>新能源</th>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>区块链</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>房地产科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             估值（亿人民币）  企业数量\n",
       "国家    行业                   \n",
       "中国    金融科技      17960    22\n",
       "      媒体和娱乐      8230    17\n",
       "      共享经济       4740     8\n",
       "      电子商务       4220    33\n",
       "      物流         3910    16\n",
       "      人工智能       2090    15\n",
       "      健康科技       2060    13\n",
       "      新能源汽车      1810    12\n",
       "      软件与服务      1460    15\n",
       "      机器人        1400     3\n",
       "      房地产科技      1340     7\n",
       "      区块链        1250     4\n",
       "      教育科技       1190    11\n",
       "新加坡   共享经济       1000     1\n",
       "印度尼西亚 电子商务        870     3\n",
       "韩国    电子商务        740     3\n",
       "中国    大数据         720     9\n",
       "印度尼西亚 共享经济        700     1\n",
       "中国    消费品         620     4\n",
       "      云计算         460     5\n",
       "      生命科学        440     4\n",
       "      新零售         360     4\n",
       "新加坡   电子商务        350     1\n",
       "韩国    游戏          350     1\n",
       "中国    网络安全        200     1\n",
       "澳大利亚  云计算         200     1\n",
       "韩国    物流          200     1\n",
       "日本    人工智能        150     1\n",
       "中国    新能源         140     2\n",
       "      游戏          100     1\n",
       "日本    区块链          70     1\n",
       "菲律宾   房地产科技        70     1\n",
       "韩国    金融科技         70     1"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从估值、企业数量和行业分布进行统计\n",
    "亚太[['国家','城市','企业名称','估值（亿人民币）','成立年份','行业']]\\\n",
    ".groupby(['国家','行业'])\\\n",
    ".agg({'估值（亿人民币）':'sum','企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename ( columns = {\"企业名称\":\"企业数量\"} )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析-亚太地区国家估值情况、企业数量与估值的相关性\n",
    "* 独角兽在亚太地区各个国家的估值对比？\n",
    "* 企业数量与估值之间有没有较强的相关性？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>2014</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>2012</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>5140</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>2012</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>2014</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>雅加达</td>\n",
       "      <td>2011</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>日本</td>\n",
       "      <td>东京</td>\n",
       "      <td>2014</td>\n",
       "      <td>区块链</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>马卡迪</td>\n",
       "      <td>2015</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>韩国</td>\n",
       "      <td>首尔</td>\n",
       "      <td>2005</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>韩国</td>\n",
       "      <td>首尔</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        国家   城市  成立年份     行业  估值（亿人民币）  企业数量\n",
       "0       中国   杭州  2014   金融科技     10000     1\n",
       "1       中国   北京  2012  媒体和娱乐      5140     3\n",
       "2       中国   北京  2012   共享经济      3600     1\n",
       "3       中国   上海  2011   金融科技      2700     1\n",
       "4       中国   深圳  2014   金融科技      1500     1\n",
       "..     ...  ...   ...    ...       ...   ...\n",
       "189  印度尼西亚  雅加达  2011   电子商务        70     1\n",
       "190     日本   东京  2014    区块链        70     1\n",
       "191    菲律宾  马卡迪  2015  房地产科技        70     1\n",
       "192     韩国   首尔  2005   电子商务        70     1\n",
       "193     韩国   首尔  2011   金融科技        70     1\n",
       "\n",
       "[194 rows x 6 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 亚太每年情况\n",
    "亚太数据=亚太[['国家','城市','企业名称','估值（亿人民币）','成立年份','行业']]\\\n",
    ".groupby(['国家','城市','成立年份','行业'])\\\n",
    ".agg({'估值（亿人民币）':\"sum\",'企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename(columns = {\"企业名称\":\"企业数量\"})\\\n",
    ".reset_index()\n",
    "亚太数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>54700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>1570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>韩国</td>\n",
       "      <td>1360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>新加坡</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>日本</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      国家  估值（亿人民币）\n",
       "0     中国     54700\n",
       "1  印度尼西亚      1570\n",
       "2     韩国      1360\n",
       "3    新加坡      1350\n",
       "4     日本       220\n",
       "5   澳大利亚       200\n",
       "6    菲律宾        70"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 估值发展趋势\n",
    "亚太估值=亚太数据[['国家','城市','行业','成立年份','企业数量','估值（亿人民币）']]\\\n",
    ".groupby(['国家'])\\\n",
    ".agg({'估值（亿人民币）':'sum'})\\\n",
    ".sort_values(['估值（亿人民币）'],ascending=False)\n",
    "亚太估值=亚太估值.reset_index()\n",
    "亚太估值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6)) #设置画布大小\n",
    "labels = 亚太估值['国家']\n",
    "sizes = 亚太估值['估值（亿人民币）']\n",
    "#设置饼形图每块的颜色\n",
    "colors = ['red', 'yellow', 'slateblue', 'green','magenta','cyan']\n",
    "plt.pie(sizes, #绘图数据\n",
    "        labels=labels,#添加区域水平标签\n",
    "        colors=colors,# 设置饼图的自定义填充色\n",
    "        labeldistance=1.02,#设置各扇形标签（图例）与圆心的距离\n",
    "        autopct='%.1f%%',# 设置百分比的格式，这里保留一位小数\n",
    "        startangle=90,# 设置饼图的初始角度\n",
    "        radius = 0.5, # 设置饼图的半径\n",
    "        center = (0.2,0.2), # 设置饼图的原点\n",
    "        textprops = {'fontsize':9, 'color':'k'}, # 设置文本标签的属性值\n",
    "        pctdistance=0.6)# 设置百分比标签与圆心的距离\n",
    "# 设置x，y轴刻度一致，保证饼图为圆形\n",
    "plt.axis('equal')\n",
    "plt.title('亚太各国家估值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>韩国</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>新加坡</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>日本</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      国家  企业数量\n",
       "0     中国   206\n",
       "1     韩国     6\n",
       "2  印度尼西亚     4\n",
       "3    新加坡     2\n",
       "4     日本     2\n",
       "5   澳大利亚     1\n",
       "6    菲律宾     1"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 亚太各国家企业数量\n",
    "亚太企业=亚太数据[['国家','城市','行业','成立年份','企业数量','估值（亿人民币）']]\\\n",
    ".groupby(['国家'])\\\n",
    ".agg({'企业数量':'sum'})\\\n",
    ".sort_values(['企业数量'],ascending=False)\n",
    "亚太企业=亚太企业.reset_index()\n",
    "亚太企业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 亚太地区企业数量与估值的相关性\n",
    "x=pd.DataFrame(亚太企业['企业数量'])\n",
    "y=pd.DataFrame(亚太估值['估值（亿人民币）'])\n",
    "plt.title('企业数量与估值的相关性')   #图表标题\n",
    "plt.scatter(x, y,  color='b') #真实值散点图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据结果-亚太地区\n",
    "* 从饼状图来看，亚太地区中的中国独角兽企业估值是最高的，且占比超过了90％，发展潜力大\n",
    "* 其他国家估值小，与中国差距巨大，可能受国家领土大小、企业数量等因素影响\n",
    "* 从散点图看，除中国位于图表右上方，其他点均在左下角，但从整体看，企业数量与估值成正相关，故可知企业数量与估值具有一定的正相关关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗-美国"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>爱彼迎</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2700</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>Brian Chesky, Joe Gebbia, Nathan Blecharczyk</td>\n",
       "      <td>2008</td>\n",
       "      <td>Tiger Global Management, Founders Fund, Y Comb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>2500</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>航天</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>2002</td>\n",
       "      <td>DFJ, Founders Fund, Google, Bank of America, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>WeWork</td>\n",
       "      <td>WeWork</td>\n",
       "      <td>2100</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>Adam Neumann, Miguel McKevley</td>\n",
       "      <td>2010</td>\n",
       "      <td>Softbank, Hony Capital, Glade Brook Capital, W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>264</td>\n",
       "      <td>VTS</td>\n",
       "      <td>VTS</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>Brandon Weber, Donald DeSantis, Karl Baum, Nia...</td>\n",
       "      <td>2012</td>\n",
       "      <td>Bessemer Venture Partners, Thrive Capital, Ope...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>264</td>\n",
       "      <td>WalkMe</td>\n",
       "      <td>WalkMe</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Dan Adika, Eyal Cohen, Rephael Sweary</td>\n",
       "      <td>2011</td>\n",
       "      <td>Gemini Israel Ventures, Scale Venture Partners...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>264</td>\n",
       "      <td>Zeta Global</td>\n",
       "      <td>Zeta Global</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>David A. Steinberg, John Sculley</td>\n",
       "      <td>2007</td>\n",
       "      <td>GPI Capital, GSO Capital Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名                   企业名称           Company Name  估值（亿人民币）  国家   城市  \\\n",
       "序号                                                                          \n",
       "3      4                  Infor                  Infor      3500  美国   纽约   \n",
       "4      5              JUUL Labs              JUUL Labs      3400  美国  旧金山   \n",
       "5      6                    爱彼迎                 Airbnb      2700  美国  旧金山   \n",
       "7      8                 SpaceX                 SpaceX      2500  美国  洛杉矶   \n",
       "8      9                 WeWork                 WeWork      2100  美国   纽约   \n",
       "..   ...                    ...                    ...       ...  ..  ...   \n",
       "472  264                    VTS                    VTS        70  美国   纽约   \n",
       "474  264                 WalkMe                 WalkMe        70  美国  旧金山   \n",
       "489  264            Zeta Global            Zeta Global        70  美国   纽约   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "\n",
       "        行业                                            掌门人/创始人  成立年份  \\\n",
       "序号                                                                    \n",
       "3      云计算                                        Jim Schaper  2002   \n",
       "4      消费品  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "5     共享经济       Brian Chesky, Joe Gebbia, Nathan Blecharczyk  2008   \n",
       "7       航天                                          Elon Musk  2002   \n",
       "8     共享经济                      Adam Neumann, Miguel McKevley  2010   \n",
       "..     ...                                                ...   ...   \n",
       "472  房地产科技  Brandon Weber, Donald DeSantis, Karl Baum, Nia...  2012   \n",
       "474    云计算              Dan Adika, Eyal Cohen, Rephael Sweary  2011   \n",
       "489   人工智能                   David A. Steinberg, John Sculley  2007   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "\n",
       "                                                部分投资机构  \n",
       "序号                                                      \n",
       "3         Golden Gate Capital, Koch Equity Development  \n",
       "4    M13, Timothy Davis, Evolution VC Partners, Tig...  \n",
       "5    Tiger Global Management, Founders Fund, Y Comb...  \n",
       "7    DFJ, Founders Fund, Google, Bank of America, B...  \n",
       "8    Softbank, Hony Capital, Glade Brook Capital, W...  \n",
       "..                                                 ...  \n",
       "472  Bessemer Venture Partners, Thrive Capital, Ope...  \n",
       "474  Gemini Israel Ventures, Scale Venture Partners...  \n",
       "489                  GPI Capital, GSO Capital Partners  \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...  \n",
       "493               IVP (Institutional Venture Partners)  \n",
       "\n",
       "[203 rows x 10 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 筛出美国\n",
    "美国=df[df['国家'].isin(['美国'])]\n",
    "美国"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>纽约</td>\n",
       "      <td>Infor</td>\n",
       "      <td>云计算</td>\n",
       "      <td>3500</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>消费品</td>\n",
       "      <td>3400</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>爱彼迎</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2700</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>航天</td>\n",
       "      <td>2500</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>纽约</td>\n",
       "      <td>WeWork</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2100</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>纽约</td>\n",
       "      <td>VTS</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>70</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>WalkMe</td>\n",
       "      <td>云计算</td>\n",
       "      <td>70</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>纽约</td>\n",
       "      <td>Zeta Global</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>70</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>半月湾</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>物流</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      城市                   企业名称     行业  估值（亿人民币）  成立年份\n",
       "序号                                                    \n",
       "3     纽约                  Infor    云计算      3500  2002\n",
       "4    旧金山              JUUL Labs    消费品      3400  2015\n",
       "5    旧金山                    爱彼迎   共享经济      2700  2008\n",
       "7    洛杉矶                 SpaceX     航天      2500  2002\n",
       "8     纽约                 WeWork   共享经济      2100  2010\n",
       "..   ...                    ...    ...       ...   ...\n",
       "472   纽约                    VTS  房地产科技        70  2012\n",
       "474  旧金山                 WalkMe    云计算        70  2011\n",
       "489   纽约            Zeta Global   人工智能        70  2007\n",
       "492  半月湾  Zipline International     物流        70  2014\n",
       "493  洛杉矶           ZipRecruiter   电子商务        70  2010\n",
       "\n",
       "[203 rows x 5 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出美国城市、行业、估值、成立年份\n",
    "数据资源=美国[['城市','企业名称','行业','估值（亿人民币）','成立年份']]\n",
    "数据资源"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2002</td>\n",
       "      <td>3500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>2015</td>\n",
       "      <td>3400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2008</td>\n",
       "      <td>2700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>航天</td>\n",
       "      <td>2002</td>\n",
       "      <td>2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>纽约</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2010</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>阿拉米达</td>\n",
       "      <td>新能源</td>\n",
       "      <td>2011</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2010</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2009</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>2001</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>2014</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        城市     行业  成立年份  估值（亿人民币）  企业数量\n",
       "0       纽约    云计算  2002      3500     1\n",
       "1      旧金山    消费品  2015      3400     1\n",
       "2      旧金山   共享经济  2008      2700     1\n",
       "3      洛杉矶     航天  2002      2500     1\n",
       "4       纽约   共享经济  2010      2100     1\n",
       "..     ...    ...   ...       ...   ...\n",
       "186   阿拉米达    新能源  2011        70     1\n",
       "187  雷德伍德城    云计算  2010        70     1\n",
       "188  雷德伍德城   人工智能  2009        70     1\n",
       "189  雷德伍德城   健康科技  2001        70     1\n",
       "190  雷德伍德城  软件与服务  2014        70     1\n",
       "\n",
       "[191 rows x 5 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从估值、企业数量和行业分布进行统计\n",
    "美国情况=美国[['城市','企业名称','估值（亿人民币）','成立年份','行业']]\\\n",
    ".groupby(['城市','行业','成立年份'])\\\n",
    ".agg({'估值（亿人民币）':'sum','企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename ( columns = {\"企业名称\":\"企业数量\"} )\\\n",
    ".reset_index()\n",
    "美国情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>成立年份</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2002</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旧金山</td>\n",
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       "      <td>3400</td>\n",
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       "      <th>2</th>\n",
       "      <td>旧金山</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2002</td>\n",
       "      <td>2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>纽约</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2010</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2010</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>物流</td>\n",
       "      <td>2013</td>\n",
       "      <td>1170</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>大数据</td>\n",
       "      <td>2004</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>2007</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>区块链</td>\n",
       "      <td>2012</td>\n",
       "      <td>900</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>圣地亚哥</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>2008</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2010</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>物流</td>\n",
       "      <td>2012</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>门洛帕克</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>2016</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门洛帕克</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2013</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Emerville</td>\n",
       "      <td>网络安全</td>\n",
       "      <td>2007</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Harrisburg</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2016</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>匹兹堡</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>2015</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>纽约</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2005</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2011</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2012</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Plantation</td>\n",
       "      <td>虚拟与增强现实</td>\n",
       "      <td>2011</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>游戏</td>\n",
       "      <td>2008</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>普利茅斯</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>2009</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2014</td>\n",
       "      <td>340</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>加迪纳</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>2014</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>圣马特奥</td>\n",
       "      <td>大数据</td>\n",
       "      <td>2012</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>坎布里奇</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>2014</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2009</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2015</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2015</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2014</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>虚拟与增强现实</td>\n",
       "      <td>2010</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2007</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>纽约</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2012</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>达拉斯</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>2010</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2011</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2012</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2013</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Foster City</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2014</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>卡平特里亚</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>2002</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2003</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>圣塔莫尼卡</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>2011</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>圣马特奥</td>\n",
       "      <td>游戏</td>\n",
       "      <td>2004</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>云计算</td>\n",
       "      <td>2014</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>大数据</td>\n",
       "      <td>2013</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>2005</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>机器人</td>\n",
       "      <td>2016</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>游戏</td>\n",
       "      <td>2004</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2017</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             城市       行业  成立年份  估值（亿人民币）  企业数量\n",
       "0            纽约      云计算  2002      3500     1\n",
       "1           旧金山      消费品  2015      3400     1\n",
       "2           旧金山     共享经济  2008      2700     1\n",
       "3           洛杉矶       航天  2002      2500     1\n",
       "4            纽约     共享经济  2010      2100     1\n",
       "5           旧金山     金融科技  2010      1600     1\n",
       "6           旧金山       物流  2013      1170     3\n",
       "7         帕洛阿尔托      大数据  2004      1000     1\n",
       "8           洛杉矶    媒体和娱乐  2007      1000     1\n",
       "9           旧金山      区块链  2012       900     2\n",
       "10         圣地亚哥     生命科学  2008       800     1\n",
       "11          旧金山     电子商务  2010       600     1\n",
       "12          旧金山       物流  2012       550     1\n",
       "13         门洛帕克     生命科学  2016       550     1\n",
       "14         门洛帕克     金融科技  2013       550     1\n",
       "15    Emerville     网络安全  2007       500     1\n",
       "16   Harrisburg     人工智能  2016       500     1\n",
       "17          匹兹堡     共享经济  2015       500     1\n",
       "18           纽约     人工智能  2005       500     1\n",
       "19          旧金山     金融科技  2011       450     2\n",
       "20          旧金山     金融科技  2012       400     2\n",
       "21   Plantation  虚拟与增强现实  2011       400     1\n",
       "22        帕洛阿尔托       游戏  2008       350     1\n",
       "23         普利茅斯    新能源汽车  2009       350     1\n",
       "24          旧金山      云计算  2014       340     3\n",
       "25          加迪纳    新能源汽车  2014       300     1\n",
       "26         圣马特奥      大数据  2012       300     1\n",
       "27         坎布里奇     生命科学  2014       300     1\n",
       "28        帕洛阿尔托     电子商务  2009       300     1\n",
       "29        帕洛阿尔托     电子商务  2015       300     1\n",
       "30          旧金山      云计算  2015       300     1\n",
       "31          旧金山     电子商务  2014       300     1\n",
       "32          旧金山  虚拟与增强现实  2010       300     1\n",
       "33          旧金山     金融科技  2007       300     1\n",
       "34           纽约     电子商务  2012       300     1\n",
       "35          达拉斯     生命科学  2010       300     1\n",
       "36          旧金山      云计算  2011       220     2\n",
       "37          旧金山      云计算  2012       220     2\n",
       "38          旧金山      云计算  2013       220     2\n",
       "39  Foster City     人工智能  2014       200     1\n",
       "40        卡平特里亚    房地产科技  2002       200     1\n",
       "41          圣何塞     人工智能  2003       200     1\n",
       "42        圣塔莫尼卡     健康科技  2011       200     1\n",
       "43         圣马特奥       游戏  2004       200     1\n",
       "44        帕洛阿尔托      云计算  2014       200     1\n",
       "45          旧金山      大数据  2013       200     1\n",
       "46          旧金山    媒体和娱乐  2005       200     1\n",
       "47          旧金山      机器人  2016       200     1\n",
       "48          旧金山       游戏  2004       200     1\n",
       "49          旧金山     金融科技  2017       200     1"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出列表前50条信息\n",
    "美国取样=美国情况.head(50)\n",
    "美国取样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析-美国城市估值\n",
    "* 美国城市估值如何？各大城市的估值排行？比较？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>旧金山</td>\n",
       "      <td>17060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>纽约</td>\n",
       "      <td>8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>3570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>2740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>门洛帕克</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>哥伦布</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>底特律</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>圣卡洛斯</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>坎贝尔</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>尔湾</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       城市  估值（亿人民币）\n",
       "0     旧金山     17060\n",
       "1      纽约      8640\n",
       "2     洛杉矶      3570\n",
       "3   帕洛阿尔托      2740\n",
       "4    门洛帕克      1300\n",
       "..    ...       ...\n",
       "58    哥伦布        70\n",
       "59    底特律        70\n",
       "60   圣卡洛斯        70\n",
       "61    坎贝尔        70\n",
       "62     尔湾        70\n",
       "\n",
       "[63 rows x 2 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 美国城市估值排行\n",
    "美国城市估值排行=美国情况[['城市','行业','成立年份','企业数量','估值（亿人民币）']]\\\n",
    ".groupby(['城市'])\\\n",
    ".agg({'估值（亿人民币）':'sum'})\\\n",
    ".sort_values(['估值（亿人民币）'],ascending=False)\n",
    "美国城市估值排行=美国城市估值排行.reset_index()\n",
    "美国城市估值排行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
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       "      <th>城市</th>\n",
       "      <th>估值（亿人民币）</th>\n",
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       "      <td>旧金山</td>\n",
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       "      城市  估值（亿人民币）\n",
       "0    旧金山     17060\n",
       "1     纽约      8640\n",
       "2    洛杉矶      3570\n",
       "3  帕洛阿尔托      2740\n",
       "4   门洛帕克      1300\n",
       "5   圣地亚哥      1010\n",
       "6  雷德伍德城       870\n",
       "7    波士顿       820\n",
       "8    山景城       660\n",
       "9   圣马特奥       650"
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   "source": [
    "美国城市估值排行取样=美国城市估值排行.head(10)\n",
    "美国城市估值排行取样"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 美国独角兽企业在美国城市的累计市值总额情况\n",
    "美国城市估值排行取样.iplot(kind=\"bar\", x=[\"城市\"], y=\"估值（亿人民币）\", asFigure=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据结果-美国城市估值\n",
    "* 由下面条形图可知，美国估值排名前十名的城市，如旧金山、纽约、洛杉矶、帕洛阿尔托等\n",
    "* 城市间估值最高与最低相差较大，多数城市估值在1k及以下\n",
    "* 旧金山、纽约作为美国大城市，人流量大、市值高，估值也远超其他城市\n",
    "* 可见美国独角兽分布较为集中，估值量大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析-美国行业估值\n",
    "* 美国独角兽企业各行业的估值？排行？势头分析？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模块准备，解决图表问题\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "from plotly.graph_objs import Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>纽约</th>\n",
       "      <th>云计算</th>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">旧金山</th>\n",
       "      <th>消费品</th>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洛杉矶</th>\n",
       "      <th>航天</th>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>2100</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">旧金山</th>\n",
       "      <th>物流</th>\n",
       "      <td>1720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>1300</td>\n",
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       "      <th>洛杉矶</th>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <th>大数据</th>\n",
       "      <td>1000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">旧金山</th>\n",
       "      <th>区块链</th>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣地亚哥</th>\n",
       "      <th>生命科学</th>\n",
       "      <td>800</td>\n",
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       "    <tr>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">门洛帕克</th>\n",
       "      <th>生命科学</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emerville</th>\n",
       "      <th>网络安全</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harrisburg</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>匹兹堡</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plantation</th>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普利茅斯</th>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣马特奥</th>\n",
       "      <th>大数据</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>加迪纳</th>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
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       "      <th>纽约</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>达拉斯</th>\n",
       "      <th>生命科学</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>坎布里奇</th>\n",
       "      <th>生命科学</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旧金山</th>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>300</td>\n",
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       "      <th>圣马特奥</th>\n",
       "      <th>游戏</th>\n",
       "      <td>200</td>\n",
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       "      <th>旧金山</th>\n",
       "      <th>游戏</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>圣塔莫尼卡</th>\n",
       "      <th>健康科技</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <th>云计算</th>\n",
       "      <td>200</td>\n",
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       "      <th>圣何塞</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
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       "      <th>旧金山</th>\n",
       "      <th>机器人</th>\n",
       "      <td>200</td>\n",
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       "      <th>卡平特里亚</th>\n",
       "      <th>房地产科技</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">旧金山</th>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foster City</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     估值（亿人民币）\n",
       "城市          行业               \n",
       "纽约          云计算          3500\n",
       "旧金山         消费品          3400\n",
       "            金融科技         2950\n",
       "            共享经济         2700\n",
       "洛杉矶         航天           2500\n",
       "纽约          共享经济         2100\n",
       "旧金山         物流           1720\n",
       "            云计算          1300\n",
       "洛杉矶         媒体和娱乐        1000\n",
       "帕洛阿尔托       大数据          1000\n",
       "旧金山         区块链           900\n",
       "            电子商务          900\n",
       "圣地亚哥        生命科学          800\n",
       "帕洛阿尔托       电子商务          600\n",
       "门洛帕克        生命科学          550\n",
       "            金融科技          550\n",
       "Emerville   网络安全          500\n",
       "Harrisburg  人工智能          500\n",
       "纽约          人工智能          500\n",
       "匹兹堡         共享经济          500\n",
       "Plantation  虚拟与增强现实       400\n",
       "普利茅斯        新能源汽车         350\n",
       "帕洛阿尔托       游戏            350\n",
       "圣马特奥        大数据           300\n",
       "加迪纳         新能源汽车         300\n",
       "纽约          电子商务          300\n",
       "达拉斯         生命科学          300\n",
       "坎布里奇        生命科学          300\n",
       "旧金山         虚拟与增强现实       300\n",
       "圣马特奥        游戏            200\n",
       "旧金山         游戏            200\n",
       "圣塔莫尼卡       健康科技          200\n",
       "帕洛阿尔托       云计算           200\n",
       "圣何塞         人工智能          200\n",
       "旧金山         机器人           200\n",
       "卡平特里亚       房地产科技         200\n",
       "旧金山         媒体和娱乐         200\n",
       "            大数据           200\n",
       "Foster City 人工智能          200"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 美国城市行业估值排行\n",
    "美国行业估值排行=美国取样[['城市','行业','成立年份','企业数量','估值（亿人民币）']]\\\n",
    ".groupby(['城市','行业'])\\\n",
    ".agg({'估值（亿人民币）':'sum'})\\\n",
    ".sort_values(['估值（亿人民币）'],ascending=False)\n",
    "美国行业估值排行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uDsjfnOkvf/kLkJ/42pJNr6297rrrmD17Nj/5yU+A/M2hzjrrLH72s58V3cfq1at56aWXiAiee+453vzmN2825tRTT2WnnXZi9erVPPfcc9x+++28+uqrjB8/noULFwIwevRofvzjH7P77rvzgx/8gNGjR3PSSSfxxBNP8NnPfnaLvW9rBtp+tnDhQi666CLOO+88SktLOffcc/nQhz5EfX09n/zkJ1m2bBltbW1d1raXlJTQ3NxctC5JkiRpYBx11FHcd999QH5J7yGHHNIxM3vfffdxxx13MHnyZNauXdvxmcbGxo6A+Mwzz3DkkUf2eIwRI0awevVqxo4dC8AnPvEJhg8f3hFor7nmGmbOnMkee+xRdB+XX345J5xwAm9961v56le/yjXXXLPZmKuuuor999+fV199lerq6i6zre1ez6N11q1bR0q9u6nWG+Vje/rZaaedxv3338/ll1/OO97xDj70oQ8BMGXKFHbffXcef/xxxo0b1yWotrS0EBFF65IkSZIG3g477LBZrbsZyje96U0cd9xxHHfccRx88MFb3O+//uu/doTk9uN0vm51wYIFXZYHL1++nFdeeaXj/f33388dd9zBpz/9aY477jheeOEF/uM//qPbYz322GO0tbVxyCGHcNNNNwGwcePGju3FHhXU2tq62bW0l156KR/5yEe2eH5bwxnafvLEE0+wbt06Jk2aRHl5OQceeCAPPvggb33rWzt+8RsbGxk2bBiVlZUsXbqU448/npQSy5cvp7y8vGhdkiRJUl5vH7OzLbWHvLa2Nn7605/ywAMPAPllvkcffTSQv9vxq6++SltbG2PHjuU973kPAC+99FLHZ4s9euewww6jtraWK664osuy3pQSzzzzDOXl5V1uKjV37lz+7d/+jerqaq677jouuugibr/99o79L168mBkzZrBs2TIuueQScrkcTz31FJ/73OcYO3YsV199Neeeey5HHXUUra2t3HPPPRxzzDEcddRR7LHHHh3X7rbfwfmRRx7hIx/5CGeeeWZHD1dffTVNTU3MnDlzm3zHxfRZoI2IXYApwO9SSs/31XGy4plnnuHLX/4yv/rVr1i3bh2PPPII48aN49lnn+WUU07huuuuY+TIkUyaNIl99tmHuXPnMn36dFauXElZWRkVFRVUV1d3W5ckSZI0cNatW8eGDRtobW3l4x//eJclx7feeisA69ev5/nnn+eoo45it9124+tf/3rH5x966CE2btzIRz/60aLHuOCCC3jkkUc2O+6b3/xmRo4cSXV1NRHBunXr2GWXXToywy9+8Qt+/etfs+uuu3Z8buedd+Y3v/kNF154Ic8//zy5XI7JkyfzyU9+kgMPPLBj3C233ML555/PK6+8wuGHHw7AlVde2bG9tbWViODAAw/k8ccf7zIbPWXKFD71qU+9gW/z9Ym+WNMcEWXAbYWfjwKHA/XAXwtD/i2ltDwirgYmA7ellL5Z+OxmtZ5MnTo1LVu2bJufQ1/47ne/y9VXX01JSQlnn30206ZNY/bs2Sxfvpy3v/3tXHDBBR13Pa6vr2fevHmMGjWKb33rW7ztbW/rsS5JkiQNRStWrOj2ySEDobW1ldbWVp9CshW6+/uMiPqU0tTuxvdVoD0MWJ9SeiAiLgH+DuySUvpKpzHHAx9IKZ0UEdcA3wLevmktpfTnno6VpUArSZIkadtasWIF+++/v/eW2Q6klFi5cuXrCrR9suQ4pfTfhQMfCrwTuAk4OiKqgeXAaUAVcEPhI7XAe4CDuqltFmgjYjYwG/KPrmm//fVgtWZNfb8cp7R0Sr8cR5IkSRosSktLaWhoYOzYsYbaDEspsXr1al555ZXXle/68hraAD4CvAj8DnhvSqkxIhYBRwE7AU8VhjcBBxepbSaltABYAPkZ2qqqqj46i22jrq66X46zrS+Af+SRR7jssstYv349p556ase6eYDrr7+e3/72t1x22WUALFq0iEsvvZTy8nIWLFjA+PHje6xLkiRJ20JraysNDQ089dRTWx6sQW306NFUVlZ2e7foYvos0Kb8WubTI+J8oCKldG9h0zJgX2ANsGOhVkr+EULd1TQAmpubOf3007n44ouJCE477TTuu+8+xo4dywsvvMC8efM6npe1YsUK5s+fT21tLQ0NDcyZM4fFixcXrUuSJEnbyg477MDee+890G1ogPRJoI2IrwCNKaVFwDjgBxGxEvg9cBxwIfnrat8DPABUAn8EGrqpaQA899xzfOUrX2H69OkAVFRU8PTTTzN27Fjmzp3LoYce2jF2yZIlzJo1i1wuRy6Xo6mpibVr1xate5G8JEmSpG2hr2ZoFwA3RMQp5EPsocC1QAC/TCndFRE7A/dGRAUwEzgESN3UNAAmTJjAhAkTaGtrY8mSJWzcuJGJEydyzz330NjYyBe+8AVuvPFGIP/83He/+90dn83lcjQ0NBStT5w4sd/PR5IkSdL2p69uCvUicOQm5QM2GdMcEVWFcRenlFYDdFfTwFm4cCEXXXQR5513Hq2trXz961/n6quvprGxsWNMW1sbpaWlHe9LSkpobm4uWpckSZKkbaHPrqHtjULwvWFLNQ2c0047jQ984AMcddRR3H///Xz4wx9m77337hJox40b1yWotrS0EBFF65IkSZK0LQxooNXg9cQTT7Bu3TomTZpEeXk5Bx54II899hhLly7lyiuvZMOGDbS0tPDyyy9z7LHHsnTpUo4//nhSSixfvpzy8nIqKyu7rUuSJEnStmCgVbeeeeYZvvzlL/OrX/2KdevW8cgjj7BkyRJyuRwA999/PzfccAOXXXYZL7/8MnPnzmX69OmsXLmSsrIyKioqqK6u7rYuSZIkSduCgVbdOuSQQ5g1axaHHnooJSUlzJs3ryPMbmrMmDEsXLiQefPmMWrUKGpqanqsS5IkSdK2EPnHxWbX1KlT07Jlywa6jR7V1fXPdaNVVdn+u5QkSZKkTUVEfUppanfbhvV3M5IkSZIkbQsuOVYXziZLkiRJygpnaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJfRZoI2KXiDgyIt7UV8eQJEmSJA1dfRJoI6IM+BXwTuDuiNgtIq6OiN9GxLmdxvWqJkmSJEnSpvpqhvYA4AsppQuA/wIOB4anlKYB+0TEvhFxfG9qfdSfJEmSJCnjRvTFTlNK/w0QEYeSn6XdBbihsLkWeA9wUC9rf950/xExG5gNkMvlqKur64vT2GbWrLmkX46zLb6HLPUqSZIkaWjrk0ALEBEBfAR4EUjAU4VNTcDBwE69rG0mpbQAWAAwderUVFVVte1PYBuqq6vul+NUVaWt3keWepUkSZI0tPXZTaFS3unAY8C7gR0Lm0oLx13Ty5okSZIkSZvpq5tCfSUiPlF4Ow64iPzyYYBK4Amgvpc1SZIkSZI201dLjhcAN0TEKcDvgV8A90REBTATOIT8MuR7e1GTJEmSJGkzfXVTqBeBIzvXIqKqULs4pbT69dQkSZIkSdpUn90UalOFkHvDG6lJkiRJkrQpb7okSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtNouXHHFFey3337sv//+/OAHP6CmpobKysqOnwkTJvCd73wHgEWLFlFZWcmMGTNYtWpVxz6K1SVJkiQNTiMGugFpa9XX13PzzTdTW1vL+vXrOfroo6mrq+P000/vGHPssccyY8YMVqxYwfz586mtraWhoYE5c+awePHionVJkiRJg5eBVpm344478r3vfY8999wTgD322INnnnmGiooKAO677z523XVXJk+ezPz585k1axa5XI5cLkdTUxNr165lyZIl3dZLSkoG8tQkSZIk9cAlx8q8yZMns99++wHw7LPP8tRTT7H//vt3bL/qqqs49dRTAWhsbGTy5Mkd23K5HA0NDUXrkiRJkgYvA622KxdddBEnnHBCx8xqY2Mjq1atYtq0aQC0tbVRWlraMb6kpITm5uaidUmSJEmDl0uOtd34zW9+w9KlS7nrrrs6ajfffDPHHHNMx/tx48Z1CaotLS1ERNG6JEmSpMHLGVptF55++mnOOussampqulz3ettttzFz5syO95WVldTX1wOQUmL58uWUl5cXrUuSJEkavJyhVeZt2LCBE088kc985jMcdNBBHfUXX3yRVatWMWnSpI5adXU1c+fOZfr06axcuZKysjIqKiqK1iVJkiQNXgZaZd7dd9/N448/zhVXXMEVV1wBwIUXXsiwYcO6BFyAMWPGsHDhQubNm8eoUaOoqanpsS5JkiRp8IqU0kD3sFWmTp2ali1bNtBt9Kiurn+uxayq2vq/yyz1KkmSJGn7FxH1KaWp3W1zhlaZZgCXJEmShi5vCiVJkiRJyiQDrSRJkiQpkwy0kiRJkqRMMtBKkiRJkjLJQCtJkiRJyiQDrSRJkiQpkwy0kiRJkqRMMtBKkiRJkjLJQCtJkiRJyiQDrSRJkiQpkwy0kiRJkqRMMtBKkiRJkjLJQCtJkiRJyiQDrTQArrjiCvbbbz/2339/fvCDHwBw6KGHUllZSWVlJQcffHDH2EWLFlFZWcmMGTNYtWrVFuuSJEnSUDFioBuQhpr6+npuvvlmamtrWb9+PUcffTRHH300EcGjjz7aZeyKFSuYP38+tbW1NDQ0MGfOHBYvXly0LkmSJA0lBlqpn+24445873vfY8899wRgjz324G9/+xuTJk3abOySJUuYNWsWuVyOXC5HU1MTa9euLVovKSnp79ORJEmSBoxLjqV+NnnyZPbbbz8Ann32WZ566ilWrlzJ0qVLOeigg5g2bRq1tbUANDY2Mnny5I7P5nI5GhoaitYlSZKkocRAKw2giy66iBNOOIHS0lLOPfdcfve733HFFVfwxS9+kQ0bNtDW1kZpaWnH+JKSEpqbm4vWJUmSpKHEJcfSAPnNb37D0qVLueuuu7osFZ4yZQq77747jz/+OOPGjesSVFtaWoiIonVJkiRpKHGGVhoATz/9NGeddRY1NTWUlJRw44030tra2rG9sbGRYcOGUVlZSX19PQApJZYvX055eXnRuiRJkjSUOEMr9bMNGzZw4okn8pnPfIaDDjoIgAceeIBnnnmGU045heuuu46RI0cyadIk9tlnH+bOncv06dNZuXIlZWVlVFRUUF1d3W1dkiRJGkr6ZIY2IsZGxB0RURsRN0fEyIhYFRF1hZ+3F8ZdHRG/jYhzO312s5q0Pbn77rt5/PHHueKKKzqeO3vYYYdx9913c8ABB3Drrbfyox/9iJEjRzJmzBgWLlzIlVdeyQMPPEBNTQ1A0bokSZI0lPTVDO2/AN9JKd0ZEd8Hvgr8LKX0lfYBEXE8MDylNC0iromIfYG3b1pLKf25j3qUBsT73/9+nn766c3qH/jAB7odP2XKFG655ZZe1yVJkqShIlJKfXuAiJuAh8mH3FeA5cBpwHeAJSml2yPio8COwEGb1lJKP+xmn7OB2QC5XG7Kdddd16fnsLXWrKnvl+OUlk7Z6n1kqVew3yvF46QAACAASURBVGK2Vb+SJEnSQKuurq5PKU3tblufXkMbEdOAMuBO4IcppcaIWAQcBewEPFUY2gQcXKS2mZTSAmABwNSpU1NVVVVfncI2UVdX3S/Hqara+v85kaVewX6L2Vb9SpIkSYNZnwXaiNgF+C7wIeCZlNL6wqZlwL7AGvKzsgCl5K/n7a4mSZIkSdJm+uqmUCOBG4GzU0pPAosjojIihgPHAY8C9cB7Ch+pBJ4oUpMkSZIkaTN9NUP7KfLLhedExBzgbmAxEMAvU0p3RcTOwL0RUQHMBA4BUjc1SZIkSZI20yeBNqX0feD7m5S/scmY5oioAo4ELk4prQboriZJkiRJ0qb69KZQW5JSehG4YUs1SZIkSZI25U2XJEmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVaSJEmSlEkGWkmSJElSJhloJUmSJEmZZKCVJEmSJGWSgVbbhZtvhk98Iv/zy1++Vr/nHqip6Tr2v/4LPvUp+PKX4dlnt1yXJEmSNDgZaJV5f/wj3HsvXHwxXHAB3HgjPP88LFsGCxdCSq+NffJJuOkm+Pa38+H16qt7rkuSJEkavEYMdAPS1ho1Cs44A97ylvz73XaDpia4+2748Idh1arXxj74IBx2GOyyS/6nuRlaWorXR48emHOSJEmStGXO0Crz9toLxo/Pv25qys/Ojh8PX/wilJZ2HfvCC/nx7crK4O9/L16XJEmSNHg5Q6vtys9+Bu97X/GZ1VdfhR13fO396NGwdm3xuiRJkqTByxlabTcefhhWrMgvMy6mtBReeeW19xs29FyXJEmSNHgZaLVdeP55+P734fOf7/m617e9Df70p/zrlOCvf4Vddy1elyRJkjR4ueRYmdfaChddBMceC/vu2/PYgw6CH/0I/vEf8zeLGjMG3vQmKCnpvi5JkiRp8DLQKvN+9zt44on8s2hvvjlfO+UUmDZt87ElJfmbRS1aBDvskJ/R7akuSZIkafAy0Crz3vnO/DNku3P44fmfzvbbL/+82k0Vq0uSJEkanLyGVpIkSZKUSQZaSZIkSVImGWglSZIkSZlkoJUkSZIkZZKBVpIkSZKUSQZaSZIkSVImGWglSZIkSZlkoJUkSZIkZZKBVpIkSZKUSQZaSZIkSVIm9UmgjYixEXFHRNRGxM0RMTIiro6I30bEuZ3G9aomSZIkSdKm+mqG9l+A76SU3gc8A3wUGJ5SmgbsExH7RsTxvan1UX+SJEmSpIyLlFLfHiDiJmBn4LKU0u0R8VFgR+AgYMmWaimlH3azz9nAbIBcLjfluuuu69Nz2Fpr1tT3y3FKS6ds9T6y1CvYbzHbql9JkiRpoFVXV9enlKZ2t21EXx44IqYBZcATwFOFchNwMLBTL2ubSSktABYATJ06NVVVVW375rehurrqfjlOVdXW/8+JLPUK9lvMtupXkiRJGsx6XHIcEcMiYqcetv1zD5/dBfgucDKwhvwMLEBp4bi9rUmSJEmStJktzdDuBXw4Ih4iP9PaWQAnADds+qGIGAncCJydUnoyIuqB9wAPAJXAH4GGXtYkSZIkSdrMlgLtRqAN+BpwL5ADDgUeBv4MFFvX+Cnyy4XnRMQc4IfACRFRAcwEDil89t5e1CRJkiRJ2kzRJb0RMQL4JvkZ03LgNvKh9lngQeBXxT6bUvp+SqkspVRV+PkxUEV+5rU6pbQ6pdTcm9rWn6IkSZIkaXu0pRnae4H30jX4pk3+7JWU0otssjy5tzVJkiRJkjZVNNCmlDZGRC0wFtiN/A2ediQ/W1sOfBx4rj+alCRJkiRpU1uaoR0PPJJSumTTDRExjPwyZEmSJEmS+l3RQBsRo4BzgJaIOLybIcN47ZmxkiRJkiT1q56WHK8HZkbEPsCFwAHAGcALhSEBjOrzDiVJkiRJ6saWlhyTUvor8NGI+DCwKqW0su/bkiRJkiSpZ1sMtBERwGEppZv6oR9JkiRJknqlNzO0KSLOB/4pIk4lf6OojeSvoR2ZUjq7j3uUJEmSJGkzw7Y8BIDWwp8fB+4A7gKOBm7ti6YkSZIkSdqSHmdoI2I+0ALsExEXAnunlO4vbGtufy1JkiRJUn/b0gxtLbAEaAL+q/Bnu9RXTUmSJEmStCU9ztCmlO6AjtnY/46I1YWZ2gDeVng9L6XU0g+9SpIkSZLUYUtLjvcF/gKsLpQ+B5SSv6b2F4XPt3b/aUmSJEmS+k7RQFt4XM8CYDjw64g4r5thI1NK/9NXzUmSJEmSVEzRQJtSSkB1RBwAXAjsA5wFNBeGDAN27PMOJUmSJEnqRm+eQ/sYcHREfDz/1hlZSZIkSdLA69VzaCNih5TST1NKSzrVRkbEaX3XmiRJkiRJxfUq0AKPRMQvI2JWRIwq1NqAE/uoL0mSJEmSetTbQHsA8E1gCvBwROyYUmojH2olSZIkSep3W3psz3nAmk6lvwM/Av7/wl2QU9+1JkmSJElScVu6KdSLwMsYXCVJkiRJg8yWAu0GYCPwajfbYtu3I0mSJElS72wp0L4dWE0+2MJrM7WGWUmSJEnSgOox0KaUPtv+OiLeBnwEeC/w3pTSqxFxRB/3J0mSJElSt3r7HNobgJ+Tn5n9t0KYHQGM7MvmJEmSJEkqZktLjtt9BjgrpXRBp9owYMG2b0mSJEmSpC0rOkMbESMj4qcAKaXngemdt6eUNqSUrunj/iRJkiRJ6lbRGdqU0oaI2KVTaWxh6XFn96WULu+b1iRJkiRJKm5LS447P65nNXDmJp+9FTDQSpIkSZL6XdFAGxHDNtm+MaX01CZj3t9XjUmSJEmS1JOe7nIcwE87vR+z6YCUUuM270iSJEmSpF7o8bE9KaUfAUTEIcDh/dGQpMHn5z//OWeeeWaXWnNzM1OnTuXJJ5/sqC1atIjKykpmzJjBqlWrtliXJEmStkZP19DOiYgS4BvkH9uzf0TctcmYUSmls/qsO0kD7s477+Scc85hxowZXer//u//znHHHceee+4JwIoVK5g/fz61tbU0NDQwZ84cFi9eXLQuSZIkba2e7nI8LyL+GbgX+DywA3AV8H3gv8kvSR7VH01KGjjXX389Z5xxBitXruyo/f73v+f222/nnnvu6agtWbKEWbNmkcvlyOVyNDU1sXbt2qL1kpKSgTgdSZIkbUe2tOT4BuB48jOxdcA0YE1KqT6ltCyl9D/90KOkAXTVVVdRVlbWpfa1r32N0tJSPvvZz/LjH/+YlBKNjY1Mnjy5Y0wul6OhoaFoXZIkSdpaW3psDymlVcCqwuvngQV93ZSkwSMiurxftmwZy5Yt42tf+xoVFRVceOGFRARtbW2UlpZ2jCspKaG5ubloXZIkSdpaWwy0ktTZww8/zJFHHsns2bMBWLt2LbfeeisTJ07sElRbWlqICMaNG9dtXZIkSdpaPS45lqRNjRkzhre+9a0d70ePHs3OO+9MZWUl9fX1AKSUWL58OeXl5UXrkiRJ0tZyhlbS6/LOd76TmpoaXnjhBXbaaSeuvfZajjnmGKqrq5k7dy7Tp09n5cqVlJWVUVFRUbQuSZIkbS0DraTX5W1vexuf//zn+cAHPsCLL77I+973Pj72sY8xfPhwFi5cyLx58xg1ahQ1NTVAfka3u7okSZK0tSKlNNA9bJWpU6emZcuWDXQbPaqr65/rBauqtv7vMku9gv0Ws636lSRJkgZaRNSnlKZ2t81raCVJkiRJmeSSY0ndcjZZkiRJg50ztJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZP6NNBGRC4i7i28HhERqyKirvDz9kL96oj4bUSc2+lzm9UkSZIkSeqszwJtRJQBPwZ2KpQOAH6WUqoq/CyPiOOB4SmlacA+EbFvd7W+6lGSJEmSlF19OUPbBnwEaC68PwQ4OiIeLMzAjgCqgBsK22uB9xSpSZIkSZLUxYi+2nFKqRkgItpLDwHvTSk1RsQi4Cjys7dPFbY3AQcXqXUREbOB2QC5XI66urq+OYltZM2aS/rlONvie8hSr2C/xQzF3wVJkiQNPX0WaLvxWEppfeH1MmBfYA2wY6FWSn7GuLtaFymlBcACgKlTp6aqqqq+63obqKur7pfjVFWlrd5HlnoF+y1mKP4uSJIkaejpz7scL46IyogYDhwHPArU89qS4krgiSI1SZIkSZK66M8Z2nnAT4EAfplSuisidgbujYgKYCb562xTNzVJkiRJkrro80CbUqoq/Pl78nc67rytOSKqgCOBi1NKqwG6q0mSJEmS1Fl/ztB2K6X0Iq/d1bhoTZIkSZKkzvrzGlpJkiRJkrYZA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJIkSZKkTDLQSpIkSZIyyUArSZIkScokA60kSZIkKZMMtJK2Oz//+c8588wze6zV1NRQWVnZ8TNhwgS+853vALBo0SIqKyuZMWMGq1at6tfeJUmS1HsGWknblTvvvJNzzjmHlFKPtdNPP51HH3204+cf/uEfmDFjBitWrGD+/PnU1tZywQUXMGfOnIE4DUmSJPWCgVbSduX666/njDPO2GKts/vuu49dd92VyZMns2TJEmbNmkUul2PKlCk0NTWxdu3avm5bkiRJb4CBVtJ25aqrrqKsrGyLtU23n3rqqQA0NjYyefLkjm25XI6Ghoa+aVaSJElbxUArabsSEb2qtWtsbGTVqlVMmzYNgLa2NkpLSzu2l5SU0NzcvO0blSRJ0lYz0Eoa0m6++WaOOeaYjvfjxo3rEmBbWlp6DMSSJEkaOAZaSUPabbfdxsyZMzveV1ZWUl9fD0BKieXLl1NeXj5Q7UmSJKkHIwa6AUkaKC+++CKrVq1i0qRJHbXq6mrmzp3L9OnTWblyJWVlZVRUVAxgl5IkSSrGQCtpyHrggQc46KCDutTGjBnDwoULmTdvHqNGjaKmpmaAupMkSdKWROfnMmbR1KlT07Jlywa6jR7V1fXP9XdVVVv/d5mlXsF+ixmKvwuSJEnaPkVEfUppanfbvIZWkiRJkpRJLjmWtF1wRlmSJGnocYZWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkiRJkpRJBlpJkiRJUiYZaCVJkiRJmWSglSRJkiRlkoFWkgbYz3/+c84888yO94sWLaKyspIZM2awatWqN1wf6r1KkqTtn4FWkgbQnXfeyTnnnENKCYAVK1Ywf/58amtrueCCC5gzZ84bqg/1XiVJ0tAwYqAbkKSh7Prrr+eMM85g5cqVACxZsoRZs2aRy+XI5XI0NTWxdu3a110vKSkZ0r1KkqShwRlaSRpAV111FWVlZR3vGxsbmTx5csf7XC5HQ0PD664P9V4lSdLQYKCVpAEUEV3et7W1UVpa2vG+pKSE5ubm110f6r1KkqShwUArSYPIuHHjuoS8lpYWIuJ11+1VkiQNBQZaSRpEKisrqa+vByClxPLlyykvL3/ddXuVJElDgTeFkqRBpLq6mrlz5zJ9+nRWrlxJWVkZFRUVr7tur5IkaSjo00AbETngppTSPxXeXw1MBm5LKX3z9dQkaSgYM2YMCxcuZN68eYwaNYqampo3VLdXSZI0FET78wS3+Y4jyoCfAW9OKR0cEccDH0gpnRQR1wDfAt7em1pK6c/FjjN16tS0bNmyPjmHbaWurn+uEauq2vq/yyz1CvZbjL8LfWdb9StJkqTeiYj6lNLU7rb15TW0bcBHgPY7gFQBNxRe1wLveR01SZIkSZK66LMlxymlZujymIedgKcKr5uAg19HrYuImA3MhvxzDOvq6rZ5/9vSmjWX9MtxtsX3kKVewX6L8Xeh72yb77Z+6xvppdLSKVu9j/7qd1v0KkmShpb+vCnUGmDHwutS8rPDva11kVJaACyA/JLjqqqqPmt6W6irq+6X42ybZabZ6RXstxh/F/pOlr5byFa/fbWcu7W1lTlz5nDXXXcxbtw4vvWtb/Gud72LRYsWcemll1JeXs6CBQsYP348QNG6JEkafPrzsT31vLZ8uBJ44nXUJEl6Q37yk5+wdu1aHnzwQebPn89pp53GihUrmD9/PrW1tVxwwQXMmTMHoGhdkiQNTv05Q/sL4N6IqABmAocAqZc1SZLekFtuuYU5c+YwYsQIKisr2WWXXViyZAmzZs0il8uRy+Voampi7dq1ReslJSUDfRqSJKkbfT5Dm1KqKvzZTP6GTw8A1Sml1b2t9XWPkqTtW1NTEwBr1qyhoaGBBx98kMmTJ3dsz+VyNDQ00NjY2G1dkiQNTv05Q0tK6UVeu4Px66pJkvRGHH/88Zxzzjn85S9/4Y477mDChAlUVFRQWlraMaakpITm5mba2tq6rUuSpMGpXwOtJEn97ROf+AS77bYbDz30EMuXL+d73/seDz/8cJeg2tLSQkQwbty4buuSJGlw6s+bQkmSNCBmzpzJtGnTmDhxIkcddRSVlZXU1+cfR5RSYvny5ZSXlxetS5KkwckZWknSkHD55Zdz9tlnExFUV1czd+5cpk+fzsqVKykrK6OioqJoXZIkDU4GWknSdu/Xv/41I0eO5PDDDwdgzJgxLFy4kHnz5jFq1Chqamp6rEuSpMHJQCtJ2u4dccQRHHHEEV1qU6ZM4ZZbbtlsbLG6JEkafAy0kqTtSl1d/9zEqaoq9ctxJElScd4USpIkSZKUSQZaSZIGme985zscfPDBHHzwwVx//fUALFq0iMrKSmbMmMGqVas6xharS5I0FLjkWJKkQWTlypU88MADPPTQQzz77LO8//3vZ/z48cyfP5/a2loaGhqYM2cOixcvZsWKFd3WJUkaKgy0kiQNIn/+85854IADGD58OBUVFey+++7cf//9zJo1i1wuRy6Xo6mpibVr17JkyZJu6yUlJQN9GpIk9QuXHEuSNIhMnDiR2tpann32WZYuXUpTUxMNDQ1Mnjy5Y0wul6OhoYHGxsZu65IkDRXO0EqSNIhMnDiRiooKTjjhBNasWcOnP/1pfv/731NaWtoxpqSkhObmZtra2rqtS5I0VBhoJUkaRG644QbGjx/Pddddx6uvvsrHPvYx2traugTVlpYWIoJx48Z1W5ckaahwybEkSYNIfX09EydOBGDYsGH84z/+I9OmTaO+vh6AlBLLly+nvLycysrKbuuSJA0VztBKkjSIjB8/nltuuYV3v/vdvPTSS9x8883U1NTwmc98hunTp7Ny5UrKysqoqKigurqauXPnblaXJGmoMNBKkjSInHTSSTz22GN88IMfZMSIEZx44olMmzaNhQsXMm/ePEaNGkVNTQ0AY8aM6bYuSdJQYaCVJGkQKS0tZcGCBZvVp0yZwi233NLruiRJQ4GBVpKkAVJX1383cKqqSv12LEmS+os3hZIkSZIkZZKBVpIkSZKUSQZaSdL/a+/M460etz/+Xs2dSqU4lShjSjlRxogQmbuucIlrjJ/h5pLZlTJzy3ALNyF1TZmJm7hJQlEZMl3iJlFRIc3T8/tjPd/O7py9T6fhnH12Pu/Xa7/23us7re/zfYa11jN8hRBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhBBCCCFyEjm0QgghhNhg5s+fT/v27fn2228BGDp0KAUFBXTp0oXp06ev3i+TXAghhFgfqmRbASGEEELkPrfddhtdu3alWbNmfP7559x5552MGjWKGTNmcM011zBs2LCMciGEEGJ9kUMrhBBCiA3ik08+4ZVXXmHs2LEAjBw5km7dupGfn09+fj7z5s1j0aJFGeV5eXlZvgMhhBC5ioYcCyGEEGKD+Nvf/kbt2rW58MILeeSRR5g5cyatWrVavT0/P58ZM2ZklAshhBDri3pohRBCCLHeTJw4kYkTJ/K3v/2NJk2acPPNN/Pdd99x6KGHrt4nLy+P+fPns3LlSmrXrl1MLoQQQqwvcmiFEEIIsd5MnjyZzp0706NHDwAWLVrE8OHD13BUlyxZgplRr169tHIhhBBifZFDK4QoNT17woIF/rtyZRg0KL0M4NVXYfhwaNAALr0U8vOlrxClJZfybp06ddhqq61W/69RowaTJk2iRYsWHHfccYQQmDJlCo0bN6agoIAJEyYUkwshhBDrixxaIUSpWLrUvx98sGQZwLffwtNPwx13wE8/+farry4fPUvSrSLrK0RCruXdPffck4EDBzJ37lxq1arFo48+yrXXXsuAAQPo0KEDX3zxBfXr16dJkyZ06tSJ3r17F5MLIYQQ64scWiFEqZg2DZo1W7sM4L334IADYPPN/TN/PixZAjVqlIemmXWryPoKkZBreXf77benZ8+eHHPMMfz8888ceuihnH766bRt25a+fftSvXp1Bg4cCHhv7uDBg4vJhRBCiPVFDq0QolT873/w+edwzjlQrRqcfjrMnVtctsceLm/duvDY+vW992jrraWvEGsjF/Nut27d6Nat2xqydu3a8cILLxTbN5NcCCGEWB/k0AohSkWNGtC9u/cG/fe/cPvt/r+orG1bWLUKatZc89hFi6SvEKWhIufdMWPKZwGnAw8M5XIdIYQQuY8cWiFEqTjwwMLfLVrAFltA06aw445ryqZNg9q1YeHCwv2XLStPTZ1c01eIBOVdIYQQovRUyrYCQmxsevaEs87yT3yLBK++6v8vvxxmzy7cN5NcFGfMGFixovD/3LnwwQfFZWaw/fbw5ZcuCwG++cZXYC1Pck1fIRKUd8uWjh07UlBQQEFBAbvvvjsAQ4cOpaCggC5dujB9+vTV+2aSlxd169alZs2a1KxZk7y8PAB69OhBzZo1adiwIe++++7qfTPJhRBiU0c9tGKTIt1KoJlWAa0Iq4PmEp99BvPmwZFHwujRUKWKp9uLL64pa9YMmjSBIUN8bt/06VCnDjRsKH2FKA3Ku2XH4sWLMTM++uij1bLPP/+cO++8k1GjRjFjxgyuueYahg0bllFeXsybNw8zY/HixatlI0aM4F//+hfvv/8+H374IaeccgrffPNNRrkQQvwekEMrNinSrQSaaRXQirA6aC5xyinQrx88+yxstx1ceSXUq1dcVrWqf3r1gqFD/XfPntJXiNKivFt2fPbZZ7Rs2XIN2ciRI+nWrRv5+fnk5+czb948Fi1alFGe9JSWNa+88kqxVxoNHjyY/fbbj9atW9O6dWsuueQS5syZk1HeUNENIcTvADm0YpMi0+qg6VYBrSirg+YKdetC377F5elk4PP8brqpbHUqiVzTV4gE5d2yY8qUKUyYMIHddtuNGjVq0KdPH2bOnMm+++67ep/8/HxmzJiRUb7TTjuVi65jxoxh2rRp5OXlUblyZa6++mp++OEHDj744NX71KtXj0mTJmWUH3bYYeWiqxBCZBPNoRWbFMnqoA88AH/5C9x/P6xcmX4VUK1sK4QQvy9q1arFtddeywcffMCAAQPo1asXy5cvp3bt2qv3ycvLY/78+axcuTKtvLzYbLPNuOCCC1i0aBH33Xcfffv2ZcWKFdSvX3/1PjVq1GD27NmsWrUqrVwIIX4PqIdWbFKkWx30xx/TrwKq1UGFEOL3Req7ctu1a0fTpk357rvv1nBUlyxZgplRr169tPLyon///qt/d+/enUsuuYQff/yROXPmrJYvXbqUSpUqUbdu3bRyIYT4PaDaTmxSpFsdtG3b9KuAanXQ9Wf0aBgwwH+nW1UatIK0EKLi8dRTT7F8+fLV/2fOnEmnTp2YNGkSACEEpkyZQuPGjSkoKEgrLy8uueQSFqUMG1qwYAH77rsvb7/9NgCrVq3i+++/p1WrVuy9995p5UII8XtAPbRikyLd6qCHHgoXX1x8FdC8PK0Ouj78+qsvQNO+ffpVpUErSAshKibjx49n1qxZnH322TzxxBNUq1aNU089lQMOOIAOHTrwxRdfUL9+fZo0aUKnTp3o3bt3MXl5MXbsWE477TQGDRpEr169qFSpEv3792ennXaiT58+TJ48mRo1arD77rvTqFEj7rzzzmJyIYT4PVBuDq2ZVQG+iR+Ai4CLgVbAyyGEG+N+DxaVCVFa0q0OWqtW+lVA8/K0Ouj6MGQIFBT473SrSoNWkBZCVEyuuuoqevTowT333EObNm0YMmQIm222GYMHD6Zv375Ur16dgQMHAlCnTp208vLi0UcfpVOnTjRq1IiGDRvyxBNP0LRpUwYNGsRll11GlSpVGD58OABNmjRJKxdCiN8D5dlDuyvweAjhCgAzOw6oHELYx8weMrMdgTZFZSGEr8pRR5HjZFodNNMqoFoddN346CMfxt2tG7z5ZvpVpffYQytICyEqJg0bNuTZZ58tJm/Xrh0vvPBCqeXlQYsWLfjhhx+Kybt370737t1LLRdCiE0dCyGUz4XMzgcuABYCU4ClwIgQwitmdhJQE9gNGJkqr2k3wAAAIABJREFUCyE8nOZcPYAeAPn5+e2eeOKJcrmH9WXBgknlcp3atdtt8DlySVeQvpkoi7ywbNly+vQZzEUXncDPP//GuHEf0bJlc8yMDh12ZerUGdx995P079+ToUNfoV27nWnb1l9vcf/9z3LwwXuw447FPVqlbdmSS/oqL5QtuaSv8kLZsbHSVgghypNOnTpNCiG0T7etPB3aPYAZIYSZZjYU2B/oGkL4yMwOBXYHdgTuSZWFEG4t6bzt27cPEydOLHP9N4QxY8pnVcQDD9zwZ5lLuoL0zURZ5IV//ctXhu7aFR55BMaN89cjvfoqDB/uC2otXw7nnw/vvgu//OI9ug0a+HEnnug94mWhazp9y4pcKmeQW/oqL5QtuaSv8kLZsbHStiSefPJJ3n33Xe666y6GDh1Kv379aNy4MYMGDWKbbbYByCgXQoh0mFlGh7Y8hxx/HEKIS8gwEfgD3isLUBtfcXlBGpkQogIwcSL89hs8/zz88IPPO/7DH9xhTRZ/uuwyMPMFtp5/HgYPdvnll7ujK4QQYtNm7ty59O3bl86dO/P5559z5513MmrUKGbMmME111zDsGHDMsqFEGJ9KE+HdpiZ3QR8AnTFhx/vB4wHCoD/AjPSyIQQFYC77vLvu+/2ubCzZvmCWzVr+veECbBqFeTn+28zfy3S9On+u3bt7OovhBCi7OnduzcdO3YEYOTIkXTr1o38/Hzy8/OZN28eixYtyijPy8vLsvZCiFykPHtA+wLDgA+Bd4HngVPNrD9wAvByBpkQooKQLAq1xx7+f9tt/TU+Z53lw4x3392HGv/2G5xwArz0kr9KqaDAe2qFEEJsuowdO5aZM2euXpxq5syZa7wPNz8/nxkzZmSUCyHE+lBuPbQhhE/wlY5XY2YHAp2B20MIv2aSCSGyz7JlPnf2ssvcqS0ogEqV3JltF9cYuftuWLTIe2p32MHnzabKhRBCbJosWbKE66+/ngcffJCZM2cCsHLlSmqnDM/Jy8tj/vz5GeVCCLE+ZHWOagjh5xDC8BDCrJJkQojsM3w4dOwIjRsXymrXhoULC/8vW1ayXAghxKZJv379OP7449l2221Xy+rVq7eGo7pkyRLMLKNcCCHWh/KcQyuEyGGSRaFeeglWrHAndfly6NLFHd0Q4JtvfJGo7bf399MWlQshhNg0ee2115g3bx7//Oc/WbZsGUuWLGHp0qWcccYZHHfccYQQmDJlCo0bN6agoIAJEyYUk5c3H374IXfddRdLly7lnHPO4aCDDqJjx478+qsPEKxcuTKTJ08GKsaqzLmmrxDlhRxaIUSpSBaFAvjkExgzBs48E3r2hNatffGnOnWgYUPIy4MhQ4rLhRBCbJqMGTNm9e933nmH4cOHc8MNN9CxY0c6dOjAF198Qf369WnSpAmdOnWid+/exeTlyfz587ngggu4/fbbMTPOPfdcXnvtNcyMjz76aI19K8KqzLmmrxDliRzaLDF1Kjz9tPdwHXmkL6aTMHq0L6Rz4YX+P/U9n5de6qvIClERyMuDXr1g6FB/jU/PniXLhRBC/H6oU6cOgwcPpm/fvlSvXp2BAweWKC9PfvzxR6644go6dOgAQJMmTfj2229p2bJlsX0rwqrMuaavEOWJHNossHCh93ade66/zqR/f7jnHp93+Ouv7gS0j68N/vZbd3yT93w++CBcfXV29ReidWv/ALRoATfdVHyfTHIhhBCbNvvuuy/77rsvAO3ateOFF14otk8meXmxww47sMMOO7By5UpGjhzJihUr+Oyzz5gwYQK77bYbNWrUoE+fPhx66KHMnDlz9f1A4arMO+20k/QVogKQ1UWhfq/88gv86U/Qpo07BQ0a+Kqx4MM0CwoK933vPTjgANh8c3cQ5s+HJUuyorYQQgghxCbF4MGDufDCC+nevTu1a9fm2muv5YMPPmDAgAH06tWLZcuWVahVmXNNXyHKA/XQZoGttvLPypXusK5cCU2bFr7js1s3ePNN33fu3MKeMID69b2nduuts6O7EEIIIcSYMeWzKvGBB4YyPf+5557LMcccwxFHHMFzzz1H8+bNAe9Bbtq0KZ9++mmFWpU51/QVojyQQ5slpk6FO+/01V+PPdad2v79fdt990Gsn1i1Cj79FB5+2Hty69fX+zxFdkk3/zvTPG/N/143lLYiYfRon2KyYgUcd5y/0/nvf4fXXoMaNeDmm2GXXXzfTPLy5B//gBde8FXN99rL9TjtNPjuO99+ySVw9NH+O5NcbBrkSj02bdo0Fi9eTMuWLWncuDFt27blvffeY6uttqJq1aoAzJw5k0qVKlWIVZmnTZvG5MmTefHFF1m6dCn5+fm89957jB49mrvvvpvGjRvz3XffrdZ38ODB9OnTh8aNGzNr1qysrCItRHkhhzYLJHNozzvPhxD37euN++LFcP/9MH48PPmk77tiBbz1Fgwc6D2zffu6cSNENkg3/7tXr/TzvDX/e91Q2oqEn35yJzXJCwMG+BoLr73mAc+pU31++mOPwbvvppeXJ198Ac8+Cwcd5Pr+5z9w663ervXpA++/D3ff7Y7rww+nl4tNg1yqx2bNmsXll1/OiBEjWLx4MR9++CH16tVj9uzZnH322TzxxBNUq1aNli1bst1222V9Veavv/6aM844g6eeeoply5bRrVs36tWrx7hx4xg3bhwPP/wwEydOpGXLlixfvpzXX3+dBx54gIkTJ/Lxxx+Xu75ClCdyaLPAV1/BIYf4HFrwV5pMnerO62WX+ffs2XDLLVC5skcsN9/ce2d//dUNGyGyQer8b/Co+qefFs7z3nzzwnneqfO/U+U1amT3HioqSluRMH26By6PPdb/P/igO65t2sB22/nnvvs8z7z8cnp5vXrlp++HH3qv8N/+5v//8x/Po3Xq+LuoO3Z0PadP9/dYp5PrFZmbBrlUj+29995069aNjh07kpeXR9++fdlnn33o0aMH99xzD23atGHIkCFUq1aNatWqZX1V5mbNmnHiiSdy+eWXk5eXR8uWLWndujVTpkyhc+fOtGnThl122YUVK1bw1ltvccYZZzB8+HCqV69OixYttMqx2KSRQ5sFqlWDN97waPaECT6EuHNn2GMP2G8/f8fn3/8O3bv7EK7Zs73Xdvp0qFnThyELkQ3Szf+eM6fQeIHCed6a/71uKG1FQrt2/lm2DB591Ov8n35yWUKtWvDll54XdtutuHzPPctP35NO8s+CBXDNNS5btMgd7IRKlXxtiMWLYdtti8tPPbX89BVlR67VYxdddBEXXXTRGrJnn3027b4VYVXmQYMGrV7luH///vzyyy/cdNNNdO3aFYAzzzyTGTNmMHPmTDp37lxMrlWOxaaKVjnOAq1aeVTyrLN8qM3hh0P16u6sJlSu7AZBpUo+d+qll/zdtLvtpjm0Ivu8/LK/aqpzZ58zl5p3a9TwPLpqVXq5KBmlrUi4/XZ3aPff3//XqVO4rVo17w1btSq9PBucfTZ8/LE7NLCmXpUqwY8/ep5OJxebFqrHyo7UVY5DCGlXM9Yqx+L3hnpos8Rxx/ln7ly48krYYQefewIesdx5Z/9du7YPxUne53nHHdnRV4hUjjkGOnQonnfBe5XA8246uSgZpa1IuPZa7/m84ALYYgufcpKwfLl/16qVXp4NnnjCF7O64QYPyqbaz8nIoqpV08vFpkVFrMdyaVXmknRt0cJH8V155ZnssAPUrdufKtGa/9//YNKkgfz8M7zzTnH5ggUbX1chKgLqoc0Cs2b5Ygjg80t22MGHh335pctC8NWPGzSA7bdPLxciGyjvlh1KW5EwZYrPmQXPB1tsAXvv7dNRwJ3AOXN8NfxWrdLLy5Pnn/fFnsCn0lSu7MOKk/y8YoUPPS0ogGbN0svFpoHqsbJDaStEZuTQZoF583zlv4UL/ffUqd4rO2GCz5V96ikfktWwoQ8xTicXIhso75YdSluRMH26j8r56Sc3RH/6yYdufvUVDBniiy9VqwY77QRdu6aXlyeffw5Dh/pqx2PHupN6/PE+X/bii+GEE3y/Qw6Bv/wlvVxsGqgeKzuUtkJkRkOOs0Ayh7ZnT58zcsYZHoHv1cuNgqpVfRtAXl56uRDZQHm37FDaioQjj/QFdbp3997OE07wYYaXXuqvdqtSBXr39n0bNkwvL0+uugomT4b/+z//37o1HHaY9xy//LK/vuW883zbTjvBUUcVl4tNA9VjZYfSVojMWAi5PX6+ffv2YeLEidlWo0Q2lXkbG5ONNW9D+qZHeaHsyKW0hdzSV3mhbMklfZUXyo7fY9pCbumbS7oKUV6Y2aQQQvt029RDmyXOOceHi4BH3u+/P70MfGjWxx/7apC9exeueCmEEEIIIYQQv2fk0GaBRx7xFeeuv97f2/f3v8PgwcVl770HM2e6M9uvn3/fcoscWiGEEEIIIYQALQqVFTbbzOdBPPggPPmkzyNaudIX/Rg61F8yX6kS/Pe/vnpk3bowYAB8+qkvaz97drbvQPyeee45OO00/7z4ostefdXfq3z55Wvmz0xykZ5cS9tc01eIXOT6690+6NzZX4kEHvQ+7DA49li3DRIyyUV6lLZlx4ABA2jRogU777wz98chh0OHDqWgoIAuXbowffr01ftmkkvfTUffskY9tFmgVSt46y24/Xb46CO49VbYZRfvub3jDhg3Dt5/Hw49FJ55xl9tcMcdvtLl5MkwcaIvGiJEefPf/xbm3eXL4eqr/dUBTz9dmEcffNDl336bXi7Sk2tpm2v6CpGLjBoFkyb56raLF8M11/iCWq+9Bvfd59OUbroJHnvMX/WUTi7So7QtOyZNmsRzzz3HqFGjWLp0KUcddRS77bYbd955J6NGjWLGjBlcc801DBs2jM8//zytXPpuOvqWB3Jos0D16j4vtlEjX+GxWjX44Qfvtd18c3joId+nbl3vkd16a5dvvrkfP2tWdvUXv19S8y74CouffFKYdzffHObPhyVLfMh8OnmNGtm9h4pKrqVtrukrRC5SqxZcey20aeP/a9SAt9/2/9tt55/77oNffnFnLJ28Xr3s3kNFRWlbdtSsWZN7772XZs2aAbD11lszbtw4unXrRn5+Pvn5+cybN49FixYxcuTItPK8vDzpu4noWx5oyHEWaN4cttnGo4Lz5/trF+bOdXn//vDbb7DPPt6TUamSf1JZuDAbWgtRmHfB34M3Z05h3k2oX9/zbia5SE+upW2u6StELtKhA+y1l//+5hsPBM2du+a7hmvVgi+/zCwX6VHalh2tWrWiRYsWAMyePZvvv/+emTNn0qpVq9X75OfnM2PGjIxy6bvp6FseyKHNEu+9ByNG+FDjww+HEHxY3ogRcOaZHvVbtMgNvx9+8GNWrvRP06bZ1V0IgMcf92HxIUDNmoXyGjU8765alV4u1k6upW2u6StELnLLLVBQ4L/r1CmUV6vmvYWrVqWXi7WjtC07br31Vk499VRCCNSuXXu1PC8vj/nz57Ny5cq08mwhfXMTObRZ4Ndfff7Y1lu7AXj88VClig813nlnOOUUH2oMsOee3ot7441w9tm+gNQuu2RXfyEmT4bPP/e8W7v2mqMGkrybSS5KJtfSNtf0FSIXefJJ+P57HyJbq5bbEQnLl/t3JrkoGaVt2TF69GgmTJjAX//6V+rVq7eGI7VkyRLMLKM8G0jf3EUObRa4917vaZ0+3VfKO+IIX5hg1Sr44gvo1AleeQWGDYPdd4ddd4WxY72ntlEjaNAg23cgfs/MmePzh3r29J627bcvHHoVgg/datAgs1xkJtfSNtf0FSIX+fJLD3hfeqmP3mrVyuerg9sNc+b4kP5McpEZpW3Z8cMPP3DppZcycOBA8vLyKCgoYNKkSQCEEJgyZQqNGzfOKJe+m5a+ZY0WhcoCvXr5fIwDD4SjjnLZokVuFJ51lju6Eyd6r+yiRTBkCPTtWyhv2DCb2ovfM8uX+6rcxx4LO+7ost128zzaurXn0Tp1PI/m5aWXi/TkWtrmmr5C5CKLFrnNcMABcPDBLuvaFZ56ysvUV1/58NeddvIF19LJRXqUtmXHsmXL+POf/8z555/PbrvtBkCnTp3o3bs3HTp04IsvvqB+/fo0adIko1z6bjr6lgdyaLPABx/AtGn+DsfnnnPZ2Wd7xTp0KFSt6s4tuCGYTi5ENlDeLTtyLW1zTV8hcpFnnoEFC/z99G++6bKTTvIexfvv9+lKvXu7vGHD9HKRHqVt2fHGG2/w6aefMmDAAAYMGADAzTffzODBg+nbty/Vq1dn4MCBANSpUyetXPpuOvqWBxZCyLYOG0T79u3DxIkTs61GiYwZUz5j1Q88cMOfZS7pCtI3E8oLZUcupS3klr7KC2VLLumrvFB2/B7TFnJL31zSVYjywswmhRDap9umHlohhBBCCCHEepFLDriCiOnJ9eCGFoUSQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTyKEVQgghhBBCCJGTWAgh2zpsEGb2E/BttvUoAxoCc7KtRCnJJV1B+pYluaQrSN+yJJd0hdzSN5d0BelbluSSriB9y5Jc0hWkb1mSS7quC81CCFuk25DzDu2miplNDCG0z7YepSGXdAXpW5bkkq4gfcuSXNIVckvfXNIVpG9Zkku6gvQtS3JJV5C+ZUku6bqx0JBjIYQQQgghhBA5iRxaIYQQQgghhBA5iRzaisugbCuwDuSSriB9y5Jc0hWkb1mSS7pCbumbS7qC9C1LcklXkL5lSS7pCtK3LMklXTcKmkMrhBBCCCGEECInUQ+tEEIIIYQQQoicRA5tDmFmdc2sdcr/Sma2Zxb1yXr+MbO8ErY1MLP8jXy9amZWbSOfcz8zqxx/1zSz/Ytsr5JsTyOvsjF1iefdzMxsY58325TmuZlZ/fLQ5fdMLEO7ZFuPopjZ5unyiJnVKSqPdcvO5aBT3Y1Z35hZnZTfzTfSOausa1uQrj5Ls0+51EFmtlkp9tmmlOdqbGY110OHmqVJk7Wco2Up97Oi/9fn2ma2tZlVXYf9G5hZk/i7lZnVi78rlaUtYWZN1laGzKxW0pbG33VK2LfGRtBpDzPbO+V/NTPb3sxONbPNo2yt+b+kvGtmldOla2mfddH6IZbzumn2a2hmu5fmnCnH5JlZo3U5pryIurXIth7lwcaodyoSWXdINnViZTrezMYU+bxrZu3jPr3SODHvpDndXcBAM7vWzK4EzgAej///ZmY7bKCulc3sBjOrZ2YdEp3M7If4faSZ7Wlmt5jZGOArM5tiZp+Z2afxvh4ocs5nzeztlPseX3Sf9dDzsxSj4bMUeVUz62xmnczsQOASoL+ZHRg/h6Q2RmZ2UmxIqmZqUM2sr5k1SxGdDZy1IfoXOX8t4EYgabxWRJ1TG9QTgLfNbJyZfRU/44BxwLEbS5eojwGPATcXkV9qZi+Y2fPx+9oo39vMbkjZr5qZtTez883sZXNnvXrcdoeZHVzkvKOL/B+3NuNjHe/n9pRr3pvm+neb2U5RbwOuNrMj47aM+WJjYmbdk2sWkfeNefkmM7vS3LF6tWgDFA2NZ+Pvx4rUMyPWcu0tzOwf5s7cg7GcNDezK8zs3MSAMbPXrTDocpKZnZPmXLub2dHx2MdKSLtmwD/iMf80s7fi+V83sylFzlnbzLYxs4JYti8ysyFWQqDKzH5OOV/Rzxtmtm3cr4OZ3ZhyaA/g8DSnPB84L6b/VbFsngT8KaZb7Uy6pNFtSzP7Q/x9mZmdvZZDBgN3pjlPqctZzF/nxfz9ekzL2sA7SVqUQu8PS9h8CjDSzFI/75lZMLMT4vFWJN/eZGZHpJw/nfH9spm1KY1+8RxbmtkvVrytTT6zzax1zE99Uw59tRTpcKOZdV3L9SsDzwEPlVbnFP6Gt+fpzpnuWpUsJZhpZh2AB8ysi5kdYWbbxd9dzOyQlP2qA+PNbGgsC5OBj4ALzez4mDbPpux/gbkNs7uZdS+ixmNAicEA8zb3lvi3AdA/5X63ir87ACPM6/4bzayPmU22QnthUDxXNSt0Oiub2dVmNimlbM/OoMZNwPFpdGtoZi+Z2RbAMcBf4qZjgV4l3NYo80BTz1imSk3Kc9sW6BLvaRtgCTA56rBnynNqFI/7k5mdFeub46OsKvCKeRvxhpmNjukw2sxeB/4DHBb3PdUKnd/hlmIrWpqghLl99bKt6TD/Eeid5rZ2JNpEybnM7N0M956k137A31O2Jc91o9sPMX26J9c2r8fviXVBtShrbmZHmdlRuL01IvlvZl2jXlm1bczss/hd09zmv9bMrkrZ/k8ze8fMPog6vpBShvpnOG3aeidX2ei9O2JNQgg/AHuvZbeHcYPglBDCl1G2LHUH88Z/Id5YtgWGATcABwL3ABcDP26grivNbBIwELgMryz3ApaYWWPgWuBI4F7gKaA77lA2ARYBr1N8IvoCoEcIYU68j+bx3BvCcrwBIF43oTKwfcq2r+KnefxfHZgQ76cFcFwI4Qkz+wtwpJkFoAA4LYTwWjxmGPCgmQ0HTo/3s42ZnYw/oxtCCGM24F4mAeOBL8zscuAC3Kl9ztzAuzSE8BhuPGDRAA4hDN6Aa5bELcArwBZRnzuC08/MHknZb0H8XgGsTJHnAd2AfYELQggfmztMo+N+K+N9bAY0AirFPHEt/pxa4fnOgJtCCGs0CqUlNsiD8ee/0rw34GhggZkdHXd7CRiO50fDjYwWQBszuwjPT1fghkZZsg2wqoj+tYH5wD7AlnhaNQMWxnKaGP8hhLAipaGsGkI4MOU8T6/l2rsDX+DlpiluXO6MP4dFwDjznp+v8QBo8gxXxPNXikqsAg7CX+ZeHzcIm8eylXBLvKftgPfNg0vLgT+FEGbE841P0f1A4FHc4P4F+BX4IMrWqB+L8N8QwiFmViOEkNQFmFmHEMLbKfstBVaYO6h98Hp1dzPbB3gjhPBq3O8EvE6ZDRwH/A+4HHgeuB4YBZQYOEjhYGBX3PFZTkzHosT8fwvwFp6OPYF7QuGiF6UqZ5Fn8GBoNeAioCpwKXBjCOF/mRQ1s//gZQBgO/MgZsKRIYSFACGER4BHUo47CG+bjg4hJOmyK/B3M1uF5+XvgNZmdhvwbdTtFuCNeI5z8eBCHTNbieepysCeIYTUOj+V34APgS4hhCXmzvTLIYSF5kGEP+LPsTvwUSwzrfG8/kBivIcQ9ow6DMTrA3DH7SozuxCvK8aHEK5JuedKwD+BIf7X+oYQrsuUtmlYQWGdmspgM9sOCHj5XwrMwm2394BL4rVvAO7DnYTb8TrjOtw2uA5oZWZnAlfH6xwe02sBXu4vB/4FvAMsjo7JrcDYeMxzQNGAeUPgSSvu09UEOoUQluHpvdTMGuC2Qa2Yj9oAjc2sZwjhLeAt8x7Lw/G8OxSvl/IptJ/2wQMh2+N1wut4+zgGwFKCd2b2XzyPJfok9w/QPISwQwhhjpndjZejeUBlMzsMOAJoamajgNeiTsvwuvbwmGZL4zPYIz6H0lKAB/NW4Pn5YNzhfjaEsNrpNrOr8WfaPj7/H4CTYxo8gSuz3Mz+iNuAK/D2rXo81+ogRuRn4EUzOx2v8+8q8tyOMg+ejAcWA/WAOsB7ZrZ1vP9fgK/NneVKwPwQQle8HkvqpRGxfV0Y89DuwPuxfTgMuCzWbYkdMCYeW8nMukT9N7b9cArwNvCpmQ2IOnUCdonpdhjQBW+XXsGf70IKy2ONqGOtMtBtrcTnEoD58XcN4FC8w+ZhvN4kXr8PXp6rxP+9gMZAxwynz1Tv5CRyaCsAIYS55pGhjA5pCOGVWPjH4IbmbnHTMLzC/imEsHhD9DCPFI4PITxv3jNzN15J5uERtUfwyut8vLLbHs9DW+IVPMCAIqetAnQ3s6TQNGA9852ZPR+vux3wWqyQt4kVbAghdAbuj0bxkiKH1woh7JHy/wLgXDO7FXgshHCPeQR+38SZjYZ8y6RxMB/e/F/gU9zp/0MIIa1Bug58iDtd4BHV5xKjNaUiK3PMI6T/AKaHEO6Nsivw3os+eEM3BW8YmgLDzOwY3KhpYGYXhhAa4kbSNcD9FFbAVwEvx3tN2BI4BM8PuwPnRcfs9eiMGIW91uvDdsD+uAHwBzyNL8B71pbjxtLOwCchhHPi/d4APAiMBP4vhHDTBlw/IzFNqqcY5pXxdEsM4zw8XRoAF+Lp9k78vYOZjcWNy67A1mbWA2gbndetYz2R5J2mJehxPN4YTsWNtg/w8r4Eb5B7hRA+NbOhwGjg6VgvJCM3zsDT8mIz+xTvfdkdOAp3/vbEG8y/A/2A2rhR9hfcYPgz7mw9aWbLo1qpEexGwL0hhJvMbA+gZwhhXVZufMw8QHcz0A53WvYLIcwzsxNx565hvOb1wHnAJ3hd1hHP+2fjeffmeK8jcUPhD8DT0RhfF04GmkSnuSnuUJ+OO5nDQgj3RwP2DjyI8E78XID3DN2LG12lKmdm1go3yr8CEgcdPH+sNLNrgMNCCJ+k0XW7EELSmz0+CZTE/LUqzf7Ee/kjcEQI4ddEHkL4COhsZlvigaZueP4eiju+IeUcZ+J1w5+AzngevRt//pmcWSisK58xs/8D/gpsa2aP4W3WbNwo3R0PRjyD58lJeJ7shTt1q++91/99AAAcYElEQVSfwvri6hDCFVG/HfBRNYm+W+PO5NshhPuj7Cozew13CseEEFIDDOmojbdta95QCKt7T8xHxcwIIQwpslvbeF9n4A7gqXjgdnoM2F4c92uP17vz8Ta4AfAiHlBoFK//HJ4ve+HO/mXAlXjAZvUzj/XznDSOEyn7pPYuVwV+wgPvN+LP9jg8YLEFHrD9H+4cjgV2AvrGz30xLd6MbfTfQwinm1mveJ0n8cBjKivwQNROIYTxMbjxeQhhinmvNOZDeyfG6z4Wz5EP1E8NCgJ3mAffHo//f8brtgV43XddCGFqpnRIJYTwQdR3CtASfxYTgTOtcPRYCzz9p8V0a4oHpB4NITxkZmeY2U7A5kDNEMKTsS7eDy9bU6NNVDOE0CG27WPxYNEDeMCjGoVBr4lRt5XAHtEOHIw7en2A94oG0KOuj0aH+mqgkZn9gpeVk/GAxZN4Wds2nv/fwL/Ne2+7hhBmm9nhwEkhhD/H8y5jI9oPsb45Gc9zbfD6u1pM07eAo81sP9xh/zJee2HcfjGFbdByM8uWbXN4vO6ueBDnMuDnEMJEM1uYst8g3HGei+etl/F69ie8nk1H2nonV5FDW4aYDyO8jMIovLGmg1IVj+4fgEe8XozRrZXArmY2MsqfCiE8gDcoDSneC9qDDAbGOtIQ+Ed0YGbgFUE/vGcS3EB4Ha9I2+GFvSFeIFbiEWQzs5kpPZz9434Js/BKZZ2J0UDM7MMUJ/OTNI3qB7jjmcrq+W6xUTwCj46+D3xrZr3xhm1h/H0LMB1vsBqHEAaGEO4yH461M26wbagzm/ArhRG1+82j2Q3xyiiY2RsUBgy2iPdwXvxfHegeDcb1wswKcKNhFtDSzA5N2fwTHuF/ETeSJgIzceNwBXAu7sAkc7kPwCPLrXCD6DE8bx6ER6IBiAbAVDNLDNvbzedVtjSPtP8cQjh1fe8Jj7j2inqMjbqsoHCY9gTcKbjNvPe9Jm4QfooPT9vczDZLjNiNTHPcGEjyz7Z4vkuuVR13tprj5a8FXoZ2xRu2qcC5IYT3cAPwmZhm9+KjIU4ysz8BhBASI6wYIYSno1N7AZ7fqwK34c/3SDzCvAteVkaHEI4178HIw9PynhDC07B6yON1uCFQHY9ijwYWhBBmmdliYGUI4c54zj64k/IQ3vuWkNomtcENPyjSm2neu7Y81RFKw0kx/Z7Ah8WdEEKYF+/9STP7GjgqhHB9POcqPK+upLCeHhnTZiSel0/A0/wnYICZ3YQ7Mt+XoEeiczugSgihXfx/MfBLqoNiZp3wZ38Z/ryT3qm38Lbir8TeJEpRzmKa/QvPNyuLppf5kOtMvd11o2EMXi6T3wXE9InG8qrYAwNuzD2SOLOJkR57TAfhztci1nSux5vZpyGEpAftuahzO7wMjMd7OeZl0LMoVwBP447/UjwQ+wjuHB8TdXgFb98a4HnsHbydTe01ORGvIy6hsLcP3AG8NN7f/ngQbAY+hPSwlP1q43XQdrgjURKt8F7gdZ6OE0KYDOwV8+LmuPH6Jl4vnEdh+ZqHOzM/E6ex4PbFWXgwfUu8zPTC64EOIYRV0eDvxJq9/o3wMlASF+N1aRLEroIPcQTPx5/H33XwgHwPADN7GK8Tt4+6LAYON+/BvRMfsfAQhdONagFFy9/heM/UH807CzbD2zdCCMl8zyRwdCneBtfDn9l3sT7dO6bNybhj/UMsAzvieXg08NcQQqahzsWIAbI/42VuWzzf1MaDDQ/Ha/bDy/E3IYT55r182+Bt1XZ4+zUKz5v/MB9hUjmm05d4Ww0edCNe5yF89MADwMe4rZaU2dUdKWbWMO7bA6/HdwMOMB9ubrjDPyvu/jYeGJqO24gL8fb/DWBqCOE4MxuTpo5+BA80P4/3ng5McfI2qv1gZn/Gy0KzqONv+FD3beN5p4UQxsUg1T7Av6O8Nd55tC/ee/0hbpOVu20TQhhhHujuj9usNSgyssd8aPodeL4MUe9p8X4r4+XnghDCF0VOv971TkVEDm0ZEkJ4GY+SYGYHAGeFEE5Ls+tNZvZmCOEefIgQsSLoUmQ/wxusovLN2bDerETfyVFPwxuTXXFDIqEFXpAuxA2utiGEE8yj8gvwSvYeoEZ0yl5IOfZevCI5Nt4feA/n2hrFdcLMjsV7r1LnRFUBqpjZH0IIz8Ue6DF4Ib4ArygGhBCmxHOcBuweQhiP9+JuZj78tjNuWN6H91ZNw4fkTlhPXXvilWEjCh3uJiGEo81sCN7rPAc37JNjXgWqhRA6rc81M/A50C0xyuPzpIihfRLe4Cd5b0783g+PpjeIx4w27xWbgTdsb+BG0v145DA5X3O8R2w7PPgwHO+h2sp8mPuJG3hPL+AGVys8L4/An9s5uIFSDTfsDsANqkZ4BP7bpDfFzG4zM1uL07TOhBC+JmUagvnQzulFemOa4k7fqXj+XE5hT+My4JuUfQ/Ae02fB3aORteWcdu5+AiEYj2b0RBahj+/j3HHYWc8nfrhgaG9iEOaYqN6JT7kdxXwFzN7JYSwKPhQ3sPM7CXccB6J9woeVeSa1fCgzCrcca4aP/1xI7d6yu77AgdFo7w2PgxwTNxWBc9XM9KnMgQf8niRmb2HP+OZ6faLjsCueF5cFD8NzOxXfNTEgnjs+3h5fTzeQzu8HJQ0/DmV2kBvM6tctMfOzKqEEFaEEN6ITuIoPP9Oxg3Vjngv4uMhhDfjMWstZ8GnsFwR73Ff8+G7tfDn+kAI4dwS9J2XEjgcn/J7TMo+pwEnmk/XAHdAF9uac4Pvx/Pmsrj/AXgaT8UdhkdxwxbzhbbuwdN1Eu5oXoMHSvqY94Y+FEJ4kQyEED4x702ZGNOxT0ybzniv0dMU9sYfh+f1wbhTeDvwLD5K4Xa8veuAD4ntgtcli4G55oHfcbiDvzQ6f12AvUMI1yfPORrrGTEf9VAPD15uGUJYp6lD5j1Fj8c06wD0xOvji+IuydzXKrgz9S3u7F2I17118Pr3CTydD8B7tBOSUSNLU2R1gc1SghwJVYHKIYT9gk9TmYD3Vg3Ee6vHx/OPxdu133ADfQsz64c/ny1CCAea2YgQwlFJfos9rUfjPbRnmq8jArAo9qCl2rO1cNvlcQqnlnyZsp3Yw3V4TJcVMc2ahxDOM+/R/W8I4YXoLAzBA3CX4W3TAnyob7LmRGl5EHcYj6Owc6IpXh4vx+2QAWb2EfBTzDqrcKe7H15mhqa01d3w53M53kbMwfM18RqEEL4ynwbQGHfGn6LQmW0HbJ2i3x/xMvyveN4W+DPaEnfwtsXLyoKYbtviDlSI95Y892JrEZjP9zyD6EBb7GHHHTHDRwRtbPvh73he+BYvu7fGNK+K1wNJMKIm8C5eHy3Hn01XfDpdvxDCx/G62bJtuuHp/SJwJl4HpbIAL7Mt8aDdFXh+3hl/bjPwgMNqNrTeqYjIoS0HYoPWGzeuDDfc7kqJaoNnqkpFZMVOhUe2p+JG46+4wf5rCcesKwV4pTYU+HcI4fSU+3gej9Reghf0KUWMm3vwym403gBODT406EDcYGyO9yZOiw5bqTGzU/EIW8B7s5KG9FcrnNMxOPh80xfiMbXx4TDJ8JdkKHF93Gj5CK+M3gTuMZ9X+w0wM4TQLWXfofiQxMp41PS1aMz0xJ2i9eUfuNF+L4UT8zMOTTNfIGUVMMnMjgshPJtp33UhhLDMzOqb2eO40dk4Xq877vjdhjdoL+MG3BZ4pVwFbyS2ZE2j/kK80XoOr/BvxXsEjgF2MR/KPR7v7W8dQhgbrzfPfIjkpfhQng2hMe587Yf3qO2PG8g74w3RZngjsBJvSK+M+Wh8dMquCyEUW6hpYxPvdyHeI9wihJCMLGiGD5PaES+TbXHjIXnm/47Hn4b3grwVQhhsZgfEyPTx4L2wJVw+H2/0TsTz1R/idZvhztOf8bz/Ld7wDcIdhO9x5+w2fE7Qn0MI38b8UoXCiP0zaa5ZJV7rn/iw7z64MbkVXp6CmW0eQng4hNDJfPRA03jNi3HDrXYI4Zs05y6Gmd2M10fTgDdi+qSW2UbAuyGEa8zsmfj/8BCHtppPM3gDr+8Sg3g/PF/vhNfr92S41zUIPmSyMj4veSlrDjleQmGwaDkxaIb3UC3H64eGeE9OQmnK2T8BC3G+Z5Qlwwn/E/UJGdqdkhyxZO70Q6QsghSN1Gkl5LsOUcdkOOo0Yn0Tz/eFmd2B9/Y3wdM4mbcIcFUIodiCM2so7XMsl+F5pRIejEgCyfXxNEmGcW8dr5+s3toCqBODmYeaD+efGEK4LgZ03gKODyHMTLleXXx4+yq8PNU179U0M+sdQhhXkr54WRuG5/FbcYO11ESn+RY8KPQaXp6SvLRTCKFJyr3Px4Mks+J9T8XLZCMKR2eMjU5Vl+iY7IXbGP+L91sJ+DoUGRkVg1XNWTMInuj4k5nNpdCYPwEPAiQjizqbB41TA1dt4nem1ZufxQ377c0D2VdHPY7Gn/1CvIycj891HRIdxFr4EPK3cMfjdbxO+gifsjEKrwN/NrNrQwg3mg/N/y2EsMh8yOyNePm9iHWjA/6M6+NOVXe8nP9SZL/f8DYSvO7fGZ832RG3OZLFmS7GAzdV8CDRlniA6H+krP0QHf4Tgf4hhP8kcvNg6tKU/f6Jl48kqPpyPN87uK10CoWjV5Je4Xrx2B9TAltJYDx19E0NvPyura7caPZDCCHEZ34snre+x+vQxrhN+ituH2yFO30DQgiPm9lXeFD2dQrrho2qW2mJAZWmeJl9Brdn5xbZbSe89/g3vOd8Al6mV+D2fDKUO5UNqncqInJoy4e/Ai+EED6F1Q3CrXilm1AJn0t2eUizUIf5nKBj8Qx6Ml74fsWHhHwBPGtmr4UQMq1mVlqOxXtmwIdQjUnZ1jrq/DgwMkZRzwLqhhD6m9l7IS6EYcVfXZBE8YrKSkUIYRg+b7MqXjAfwXswrgLuDL7AQ/fYGCXOVT3cIXgfr3y6m9nbuCM5Aq/wp8Re0MHRyb4+hDAt5dJX4MGHSeYLc30b5ctCKYYZru224ud0vHEDr3SLYb6q65O4YfY1vlrp9BDCxA3UwRXxXo2X8XRJ5hqPwBvAl80XQynADc1BUe9+eGDlKAoXRNgdH378Bl7x18AN6Em40TA4hPBSyn0l87uI5x6LRzO/3sBb+h438GbgEcq5uPH2PN5LmwxlOwl3KqriBlAVik8NKBPMV0gciBslS4BBZnZ40uNpPhR6b9x5/TQaJZPxyHGySutTeKP0kvncpznmQ2m/i9e4O4SwFWkIIUw1s/Px+mMf/P5vxJ/pVODUaCwfjweyfsYNvZPx+uoDPI23MX8dx1/wYNcheES5BXBlNO5b4osQLYpOwue4M3M4/gy+x3taVucH8+Fvj+DD2hMa4yu7/ymUMMw+6jMI+CiEcGWU5eHl+UrzHphLcSP2SzNrG5/Bdfh8tR4xjRaZ2XzcoExWdn4AN+7+HfUv9eiY2DO7T9Sn2JDjFDoRpxeksDnrWM7MV9K/ywqHt9fGHcVr4qcS3uuUbpTJlin1f6sibcH6jgh6G3fQl+GG33W4QXlQyj5j4qcu3kO+Z+z9vIfSvZnhDrxMNcNHNXTE13ZYhhv9/fD8thyvA46KParNgctC4XQZ8DzaJtaNq/DAUdGe/tnAocHnyaX20FZiLelkZvvieWj/WNbOMF8o6e5S3Odqgs/NXE6hk5fw15TfO1I4j30ZbiDvhff6HBN/16Fw9dmt8bJ3Mv48knmUBfhQ16JTbqrigd9p8d5q4T2Ax5nPV90SLzfJ+hZXFzm+Hl7HXBFCeNV8eGZXCkd1VcedkL2scD5iD7xnbZ8QwgsxLV7Ce2WTzoSbo/6zgX+FEJanXPMI4iJLKWl5qPl85fEhhNfNpyiNxUc6vIY7i53wxSPXaapX8OGt3+BTW5KpWj/jgYZUqlM4imcJ3h5NA24pcs0j8SDGQtwePBe3ifpTGDTCfFG01qH4XO6f8cDmDPO56xdSuNBmNbwOPxHPHzviwavv8Wc1OoRwiflbIArMF3hL1X86hcOewR2sl0tInrK0H96M174w7tcQD2qvNJ9G1g7P97eZD1Ffji8a1QnP/9m0bbrhbdJVIYSHzYPYa6x5EHx05bd4ALoZHsA/IF73A3wRv9XzvDdWvVPhCCHoU4Yf3Nj/CHdELsENxnvxCuzkuE9SsZ2XctyYDOfrhPeSVYr/nwK230i61sB7KGvhBuiQIttfwIfkgFcM++Hzj9rjxt0D+HBYcGNsdtR1Cu5ADMSdy3FxW/466tcOrxTuwIeIVAL+D+85+UOa/fcGbk0j3xp//U7/qHdyT0OS3/F/G+C1+HszPDpfpaTns4730wof1lYdb1T3AQbFbQ/jC/RcildoXwF/TDk2GSZ6M7DtRnr+u+KN4+nx0zdJ1/i8u0Q9a+JDkqrgDdaLeAW7Fz7Xpzle0Tcvcv5bgQPj78q44/Mq3tD9GTd2b4rP+C/AlhvhnvrgQ8Ma4MbeKTF/vxzz+Edx29G4IXBrzDcjcAOsy8ZI2zR6JUOBTk+RnRjTYMf43/B645Oo77Z4NH8C0L7I+V6KaXg08ESK/IkSdKiGB6dOwntn3onP7t941PmdmCePT/SMefVPeDS3Ssq5tscNnvZ4UIiYH5Lfg/GpAE3xyHfbqOuZeJChWkz7m+Lz2AzveTggHt+WWB/hC218DTTIcF/jo54Hp9lWPX5vgS8UBx4UHJfkN9wIGIwH6sDrmLkx/yyL6XM67jB3KvosSkhvw4djJv8vLvL8k2DK/vjoggLcmLotptWrKXmjVOUsjQ5tgftLqW9eyu/3i+h+PnBKmmOuIqWeKrJtAJ7vD6fQab0Gr8sGF9m3IF7zuJS8+nLRey1yTE28ju4Y/28N7JWy/cmYBz/C27IjYn79Bu9RGYf30KSeczO8LLwedX0er+/2yKBDF2KeL0X6noQvJtOkyPXewYNUvWNeTl7R9jUeCEr+v4c74Mmxr8a80zblMzluqxfvYQTeA3c9Xtd8gBvyJ+G9jcfj7WBlvG1qgLdFl+H1cou13NMF+NSq5Pd1yTF423ES7qR2xUdGJMc1wOuwVngA4jQ80PaP+N0WLy8v4G1OHh54fS7e8+34/MLL4/m2wuuWt3HHuTbec/w+MX/igZ1n8F7Sv+D1/nVx29/w9mkPvB6qE/NBB3z+9bV4fVBAtONK+cy74z1pJ+G9w9fG/LQ97mQ9EfcbkVJnjIjPJUnXi/Dh881xR+3M+HxewO3Nwbit9D5eJ+fj842L2Vsxbf4AXJJm2/a4w2t4+987ZdupeL14BJ5fL4rp+QJeNyXPvCuev2rg7Vi1taTPRrcfcDumVXyOg/A66HYK7b6OeJt0AF4ntcfLV934v2fMP1mxbfARNsTnWQkfcdQoyibgQ5tPxOvXd/AyfTbu3I7B65BbU863tnpnnezyivTJugKb+gc3CBOnpAPeoNbBh2L0iPvUwntDUo+bkOZcd+MVeOMU2bP4SrwbQ9cOeAQJ3JkbkrLtT7gDVQuv0CfjvSdJxbVFrCQeiv8bUWiA7o/37N6fUokcig8dLK1u9eK97p5m21Z4o1unyP4TSW90bY83WPvHymA03mhMxRv9V/EGZCugTTxmIB7Nyvh81iO9r8d7Js7BjbVRxOAEPkzkVdyoGQ7skOb42rgBWSqDei26PBHT4ZP4nD/Ge8BH45V7b9xYa43PlbkWr6TvxYcv/hFfqCg531Ci8Z0i64evqJr8T+aEHoIb8A2ivDreWP5pI9xXP7zxvwiP9NfBG5Xe8dq9cKNlAt4D1g8P1LyGV/pnb4yyVUSnU/Ce/mLOctRxOh5lfSKmUW08MjwGb2y3jvomeeW2eH8P4r0k31DoMMwsQY8T8SkA4I3srvH32PjdFi/HxwNnpBx3MinBtyLn3AfvRUieY72U35Vwx+yAlPNch79qKDn+r3gPF0mex+eCjcJfdZDst0UJ9/UzXo7Tff6DzxdP9q2K12NNi5yjJ4UBxz/i5fSEmPYnxnPNwh3cq0r53NvjBmgm3d7CVxYuelx/vHcl3TnXWs5S5EfhdcpNpdG3yLFJT0SShl/hw1lT97kL73kqVlfF7YOAnYvItsUNtXPj/0q4Y/MvYr2GG5IT8UXIStJxCzIHggfhhl0toFWKfF8Kgy7N8XnFiR6j4r1ejK98mxyzB+6E1In/X42fkbiT+UX8/R/gzQz67IyX72JBGdz47wvsso7PaDRe5lPz1A9x2yV4nTo93tePMf/+hPfIzcPnX44jlm3cGfoEf0VJct9nZLh243jvk4FmGfZ5OubxMfHzTcq2J3EboQHeC3g6HugwPPB4WJFzXRiPqYPXldPi5yC8rrk/PrdGRY7Lx52Pmnj9cz4epGhI4XDOP+Lt33bxfAXxWIv3cFD8fxI+xLPzOjyjGrhdMR4P+DXEFyMDD/Y9l+Sp+N0Ln36wJx7QGIPbKVvF61+Kl41DcHuiBd6BsAUe+DsAb5/PyaBPw3iv16fZdh7uCF+G15FVYxo8F+Xp6qpueA9m8oxfiWl6NXDtOqTTRrMf8PrzIdz5bonXU2/g5eNt3DnfDW/vGuPl4854bE28E2qnstBtHcv3Z3jw4YYU2TN4vXwunucb4fbL+cCTKfVav7KqdyrSJ/H8RQ5ga59jW5bXXr2QiZlVC77gSoUmm+lVGuKcoxWJjmZWPYSwdC2HZR2zjb9QUjYxX4k1L8QVcMvhelXxVyoUHWaWbK8V/N2ZVUJcSTsOnauUUgYzPgMzqxrWHFaX05i/A7sB8NmGlueYjlaR64WyIg77bAR8UZHu38xqhpRXzlmaRbPK8NpV8YBKSa8D2pDzV4g6Pd5ntRDfHZyp/kiVx7LSKBQfYp3pGnlllY5prlVm6Vq0XV7bvmVpC5WmrTVfjX9+/F0P7+AocZ55keOr4vebLj9UwnsTZ6XIaiX5aB2uUQ9/b2227NdNxmYpoexWx6fChaL743VchbfZNxQ5tEIIIYQQQgghcpLSLLAghBBCCCGEEEJUOOTQCiGEEEIIIYTISeTQCiGEEBUMM+ttZlXNrIr5+2ITefVs6iWEEEJUNPQeWiGEEKICEd+TWyv4u4fPAE42s5X4KqM/4yucJvsOxFc2rQZsHkK4PgsqCyGEEFlDDq0QQghRQTCzZvirHvYHCCE8jL+WLBO/4q9eWYo7tUIIIcTvCg05FkIIISoOO+Lvwb3fzPZNt4OZbWFmT8W/y4BVQPK6lSpm9pmGJgshhPi9oB5aIYQQooIQQnjdzA4EWgKfmtlEYAFQGagLzAfGATub2Q5pTtEO+KYivP9UCCGEKA/k0AohhBAVBDPbAhgMPB1C+BVoH+VNgbtCCMfH/wuBE9Oc4jhgWDmpK4QQQmQdDTkWQgghKg7HAfeXYr/ngM5FZJWAY4AXN7ZSQgghREVFDq0QQghRQQgh/BMYU4r9PgEOKSJeBbQPISwuA9WEEEKICokcWiGEEKJiYWlka7yL1syqhhBWFN0phLAwLgylRaGEEEL8LtAcWiGEEKJiUT1+ADCzJsAI4KEo6g70MLPl8f8hKft2xdv2R4D7ykVbIYQQIotYCCHbOgghhBBCCCGEEOuMhhwLIYQQQgghhMhJ5NAKIYQQQgghhMhJ5NAKIYQQQgghhMhJ5NAKIYQQQgghhMhJ5NAKIYQQQgghhMhJ5NAKIYQQQgghhMhJ/h8uujDvgLlpQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 美国城市企业估值情况\n",
    "x=美国取样['行业']\n",
    "height=美国取样['估值（亿人民币）']\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.grid(axis=\"y\", which=\"major\")  # 生成虚线网格\n",
    "plt.xlabel('行业') #x、y轴标签\n",
    "plt.ylabel('行业估值')\n",
    "plt.title('美国城市行业估值情况') #图表标题\n",
    "plt.bar(x,height,width = 0.6,align='center',color = 'y',alpha=1,bottom=1.2)\n",
    "#设置每个柱子的文本标签,format(b,',')格式化销售额为千位分隔符格式\n",
    "for a,b in zip(x,height):\n",
    "    plt.text(a, b,format(b,''), ha='center', va= 'bottom',fontsize=12,color = 'k',alpha=0.9)\n",
    "#图例\n",
    "plt.legend(['估值（亿人民币）'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据结果-美国行业估值\n",
    "* 由下面图表可知，独角兽企业在美国地区中，云计算行业估值是最高的，消费品、共享经济、航天等估值次之\n",
    "* 美国科技行业发展势头好，符合当今数据时代发展的特点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析-美国城市估值以及企业数量随年份发展趋势\n",
    "* 2002-2017年间各城市估值变化？企业数量变化？\n",
    "* 发展趋势如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"15\" halign=\"left\">估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成立年份</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Emerville</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foster City</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harrisburg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plantation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>加迪纳</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>匹兹堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卡平特里亚</th>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣何塞</th>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣地亚哥</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣塔莫尼卡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣马特奥</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>坎布里奇</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旧金山</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普利茅斯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洛杉矶</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <td>3500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>达拉斯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>门洛帕克</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            估值（亿人民币）                                                       \\\n",
       "成立年份            2002   2003    2004   2005    2007    2008   2009    2010   \n",
       "城市                                                                          \n",
       "Emerville        NaN    NaN     NaN    NaN   500.0     NaN    NaN     NaN   \n",
       "Foster City      NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "Harrisburg       NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "Plantation       NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "加迪纳              NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "匹兹堡              NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "卡平特里亚          200.0    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "圣何塞              NaN  200.0     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "圣地亚哥             NaN    NaN     NaN    NaN     NaN   800.0    NaN     NaN   \n",
       "圣塔莫尼卡            NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "圣马特奥             NaN    NaN   200.0    NaN     NaN     NaN    NaN     NaN   \n",
       "坎布里奇             NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "帕洛阿尔托            NaN    NaN  1000.0    NaN     NaN   350.0  300.0     NaN   \n",
       "旧金山              NaN    NaN   200.0  200.0   300.0  2700.0    NaN  2500.0   \n",
       "普利茅斯             NaN    NaN     NaN    NaN     NaN     NaN  350.0     NaN   \n",
       "洛杉矶           2500.0    NaN     NaN    NaN  1000.0     NaN    NaN     NaN   \n",
       "纽约            3500.0    NaN     NaN  500.0     NaN     NaN    NaN  2100.0   \n",
       "达拉斯              NaN    NaN     NaN    NaN     NaN     NaN    NaN   300.0   \n",
       "门洛帕克             NaN    NaN     NaN    NaN     NaN     NaN    NaN     NaN   \n",
       "\n",
       "                                                                 \n",
       "成立年份          2011    2012    2013   2014    2015   2016   2017  \n",
       "城市                                                               \n",
       "Emerville      NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "Foster City    NaN     NaN     NaN  200.0     NaN    NaN    NaN  \n",
       "Harrisburg     NaN     NaN     NaN    NaN     NaN  500.0    NaN  \n",
       "Plantation   400.0     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "加迪纳            NaN     NaN     NaN  300.0     NaN    NaN    NaN  \n",
       "匹兹堡            NaN     NaN     NaN    NaN   500.0    NaN    NaN  \n",
       "卡平特里亚          NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "圣何塞            NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "圣地亚哥           NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "圣塔莫尼卡        200.0     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "圣马特奥           NaN   300.0     NaN    NaN     NaN    NaN    NaN  \n",
       "坎布里奇           NaN     NaN     NaN  300.0     NaN    NaN    NaN  \n",
       "帕洛阿尔托          NaN     NaN     NaN  200.0   300.0    NaN    NaN  \n",
       "旧金山          670.0  2070.0  1590.0  640.0  3700.0  200.0  200.0  \n",
       "普利茅斯           NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "洛杉矶            NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "纽约             NaN   300.0     NaN    NaN     NaN    NaN    NaN  \n",
       "达拉斯            NaN     NaN     NaN    NaN     NaN    NaN    NaN  \n",
       "门洛帕克           NaN     NaN   550.0    NaN     NaN  550.0    NaN  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 估值发展趋势\n",
    "年份_pivot=美国取样.groupby(['城市','成立年份'])\\\n",
    ".agg({\"估值（亿人民币）\":[\"sum\"]})\\\n",
    ".reset_index()\\\n",
    ".pivot(index=\"城市\",columns=\"成立年份\", values=[\"估值（亿人民币）\"])\n",
    "年份_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 城市市值发展趋势\n",
    "年份_pivot.T.plot(linestyle='-',marker='o',mfc='w',figsize=(16,8))\n",
    "plt.xlabel('成立年份')        #x轴标题\n",
    "plt.ylabel('估值')         #y轴标题\n",
    "plt.grid(axis=\"y\", which=\"major\") \n",
    "#设置x轴刻度及标签\n",
    "dates=['2002','2003','2004','2005','2006',\n",
    "       '2007','2008','2009','2010','2011',\n",
    "       '2012','2013','2014','2015','2016',\n",
    "       '2017']\n",
    "plt.xticks(range(1,20,1),dates)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>Emerville</th>\n",
       "      <th>Foster City</th>\n",
       "      <th>Harrisburg</th>\n",
       "      <th>Plantation</th>\n",
       "      <th>加迪纳</th>\n",
       "      <th>匹兹堡</th>\n",
       "      <th>卡平特里亚</th>\n",
       "      <th>圣何塞</th>\n",
       "      <th>圣地亚哥</th>\n",
       "      <th>圣塔莫尼卡</th>\n",
       "      <th>圣马特奥</th>\n",
       "      <th>坎布里奇</th>\n",
       "      <th>帕洛阿尔托</th>\n",
       "      <th>旧金山</th>\n",
       "      <th>普利茅斯</th>\n",
       "      <th>洛杉矶</th>\n",
       "      <th>纽约</th>\n",
       "      <th>达拉斯</th>\n",
       "      <th>门洛帕克</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">企业数量</th>\n",
       "      <th>2002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "城市         Emerville  Foster City  Harrisburg  Plantation  加迪纳  匹兹堡  卡平特里亚  \\\n",
       "     成立年份                                                                    \n",
       "企业数量 2002        NaN          NaN         NaN         NaN  NaN  NaN    1.0   \n",
       "     2003        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2004        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2005        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2007        1.0          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2008        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2009        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2010        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2011        NaN          NaN         NaN         1.0  NaN  NaN    NaN   \n",
       "     2012        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2013        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "     2014        NaN          1.0         NaN         NaN  1.0  NaN    NaN   \n",
       "     2015        NaN          NaN         NaN         NaN  NaN  1.0    NaN   \n",
       "     2016        NaN          NaN         1.0         NaN  NaN  NaN    NaN   \n",
       "     2017        NaN          NaN         NaN         NaN  NaN  NaN    NaN   \n",
       "\n",
       "城市         圣何塞  圣地亚哥  圣塔莫尼卡  圣马特奥  坎布里奇  帕洛阿尔托  旧金山  普利茅斯  洛杉矶   纽约  达拉斯  门洛帕克  \n",
       "     成立年份                                                                       \n",
       "企业数量 2002  NaN   NaN    NaN   NaN   NaN    NaN  NaN   NaN  1.0  1.0  NaN   NaN  \n",
       "     2003  1.0   NaN    NaN   NaN   NaN    NaN  NaN   NaN  NaN  NaN  NaN   NaN  \n",
       "     2004  NaN   NaN    NaN   1.0   NaN    1.0  1.0   NaN  NaN  NaN  NaN   NaN  \n",
       "     2005  NaN   NaN    NaN   NaN   NaN    NaN  1.0   NaN  NaN  1.0  NaN   NaN  \n",
       "     2007  NaN   NaN    NaN   NaN   NaN    NaN  1.0   NaN  1.0  NaN  NaN   NaN  \n",
       "     2008  NaN   1.0    NaN   NaN   NaN    1.0  1.0   NaN  NaN  NaN  NaN   NaN  \n",
       "     2009  NaN   NaN    NaN   NaN   NaN    1.0  NaN   1.0  NaN  NaN  NaN   NaN  \n",
       "     2010  NaN   NaN    NaN   NaN   NaN    NaN  3.0   NaN  NaN  1.0  1.0   NaN  \n",
       "     2011  NaN   NaN    1.0   NaN   NaN    NaN  2.0   NaN  NaN  NaN  NaN   NaN  \n",
       "     2012  NaN   NaN    NaN   1.0   NaN    NaN  4.0   NaN  NaN  1.0  NaN   NaN  \n",
       "     2013  NaN   NaN    NaN   NaN   NaN    NaN  3.0   NaN  NaN  NaN  NaN   1.0  \n",
       "     2014  NaN   NaN    NaN   NaN   1.0    1.0  2.0   NaN  NaN  NaN  NaN   NaN  \n",
       "     2015  NaN   NaN    NaN   NaN   NaN    1.0  2.0   NaN  NaN  NaN  NaN   NaN  \n",
       "     2016  NaN   NaN    NaN   NaN   NaN    NaN  1.0   NaN  NaN  NaN  NaN   1.0  \n",
       "     2017  NaN   NaN    NaN   NaN   NaN    NaN  1.0   NaN  NaN  NaN  NaN   NaN  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 企业数量发展趋势\n",
    "企业_pivot=美国取样.groupby(['城市','成立年份'])\\\n",
    ".agg({'企业数量':'count'})\\\n",
    ".reset_index()\\\n",
    ".pivot(index=\"城市\",columns=\"成立年份\", values=['企业数量'])\n",
    "企业_pivot.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 企业市值发展趋势\n",
    "企业_pivot.T.plot(linestyle='-',marker='o',mfc='w',figsize=(16,8))\n",
    "plt.xlabel('成立年份')        #x轴标题\n",
    "plt.ylabel('估值')         #y轴标题\n",
    "plt.grid(axis=\"y\", which=\"major\") \n",
    "#设置x轴刻度及标签\n",
    "dates=['2002','2003','2004','2005','2006',\n",
    "       '2007','2008','2009','2010','2011',\n",
    "       '2012','2013','2014','2015','2016',\n",
    "       '2017']\n",
    "plt.xticks(range(1,20,1),dates)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据结果-美国城市年份估值\n",
    "* 由折线图可知，美国独角兽企业多数城市估值随没有年份变迁而发展变化\n",
    "* 旧金山发展情况最明显，波动较大\n",
    "* 旧金山企业数量在2010年增长数量达到4家，之后逐年下降\n",
    "* 美国除旧金山其他城市波动不大，较稳定，估值也相对较低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "* 亚太地区各国家估值情况分级明显，差距甚大\n",
    "* 亚太地区各国家独角兽的企业数量与国家估值之间是一个正相关关系\n",
    "* 美国各大城市的估值情况分级大，旧金山、纽约估值情况最好\n",
    "* 美国的科技行业估值最好，符合数据时代发展的特点\n",
    "* 除旧金山外，美国其他城市的城市估值以及企业数量基本不随年份的变迁而发展改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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